Showing posts with label CALL for LINGUISTICS. Show all posts
Showing posts with label CALL for LINGUISTICS. Show all posts

Friday, December 24, 2010

INTERDISCIPLINARY: COMPUTER ASSISTED LINGUISTIC RESEARCH AND THE DEVELOPMENT OF LSP COURSEWARE


Marina Dodigovic
Bremen, Germany

ABSTRACT
The properties of German as a special language of mechanical engineering are examined on the basis of a newly compiled corpus including samples from all important disciplines and discourse styles. The syntactic, semantic, and pragmatic aspects of passive voice are the chief research goal, necessary in order to evaluate the existing and to develop new courseware. Other linguistic and metalinguistic features of the sublanguage will be investigated and transferred into teaching theory and practice.
LSP —TRANSLATION — TEACHING, CALL
What is LSP (Language for Special Purposes)? Why should it be a subject of linguistic research? How does that affect teaching? What are the implications for CALL? Research goal: To transfer the intuitive knowledge of an LSP specialist into linguistic ten-ns and make it common.
INTRODUCTION
In our world of specialization and subspecialization with partly still existing traditional division into arts and sciences with sacrosanct and institutionally defended barriers between them, we desperately need bridges. Objective reality is one, and so should be the knowledge of it. The walls that were raised between areas of knowledge were maybe necessary at a certain stage of development, but they should always be subject to the centrifugal and centripetal motion of dialectic change. The structure of knowledge is easier to change than the structure of institution. The people who start the change have to reckon with resistance toward it. CALL people made an attack at institutional thinking, and some of them still feel the bitter consequences. In that relentless fight they have, however, learned to build bridges, to unite their efforts with other brave people from different fields in order to integrate that magnificent flow of free thought into the institution itself. At the same time LSP people were fighting their own isolated fight until they discovered CALL. An obvious drawback of this liaison was that they were both equally ignored by the institution. An advantage was that they were able to build so many bridges to a vast number of sciences as to make them invincible forever. In support of that I would like to tell a true story of a bridge between LSP and CALL. LSP teachers are desperately courageous people who wander in no man's land between language and a vast diversity of scientific disciplines to be described by the kind of language they are supposed to teach, which - both the sciences and their means of expression - they mostly do not know. They, the teachers, are a free target for both, the
so called serious linguists and the specialists in those fields of knowledge whose language is at issue. As if that were not enough, LSP teachers prefer to use the computer as a teaching or learning medium. They add one more discipline to the whole lot, as if to show that they are highly aware of the interdisciplinary status of their particular subject. Indeed, the fact that they have crossed the border of traditional science divisions and faced another world of ideas makes them ready to look even further and possibly in another direction.
The question what Language for Special Purposes really is, remains yet to be answered?  So far, every LSP teacher has had to develop his or her own approach, which is not only a terrible waste of time, but also a waste of many brilliant ideas that come up from teacher intuition. Unfortunately, stiff institutional frameworks have suppressed the research which has been badly wanted for years, ever since the teachers have been forced to teach LSP without having been given a fair chance to find out what it is. My story does not differ very much from the mainstream. I started as a technical translator with very good general education and specialization in languages. Soon I discovered that my knowledge of language does not help me very much in LSP, if at all. What I could rely on was my high-school knowledge of physics and chemistry and my colleagues, technicians who with lots of patience on both sides, tried and succeeded in teaching me the basics of technology. This is how I was able to do my job in a satisfactory way. After several years of practice as a translator I became a lecturer of LSP at a technical university. My subject was German for mechanical engineers. With students LSP was
the most detested subject, not without reason. The educational authorities of the country decided simply to make it a compulsory subject at all universities. The educational goal was to train students to read the publications in their domain. There was no serious research behind these goals. It was primarily a political decision. The teachers found themselves in trouble. The method seemed to be dictated by the set goals: grammar-translation method. The language teachers were not trained to make more out of technical texts. The texts were selected to promote reading comprehension, and comprehension was what most teachers desperately wanted themselves. I found the knowledge I gained as a translator most useful in this matter. I was able to pick out relevant texts which even I understood. That made interaction between me and the class possible. I did everything in my power to arouse their interest and they responded. At that time I discovered the fascination of the computer. I discovered the potential it had as a challenge for my students. So I started using it. We all had lots of I un experimenting with new ways of teaching and learning. The miracle occurred spontaneously as a result of interaction of different powerful factors. I decided to use the computer where everything else failed. It was a very special and clearly definable problem. The passive voice is a grammatical structure that occurs ceaselessly in German technical texts. Contrastively, the passive voice is very difficult for Croatian learners since in Croatian there is no language immanent passive structure. The semantics as well as the pragmatics of passive had never been taught to my learners. Their teachers were instructed to follow the structuralist views, so they were under the impression that passive was equivalent to active. Having a choice between what they thought were two semantically and pragmatically equal structures they preferred of course, the active, the way of expression in their native language. Another difficulty was the fact that German passive voice is formed with the same auxiliary verb as the future tense. Add to this the particularity of the German sentence which puts a lot of lexical information at the end, e.g. the main verb in case of either the passive voice or the future tense, and there you have complete failure to recognize the passive voice at all on the part of an intermediate stage Croatian learner of German.
After numerous failed attempts to teach them the passive voice I decided to write a computer program which would get their minds working on it. It was a neat little program based on principles of programmed learning, pragmalinguistics, and some psycholinguistic theories. As if by miracle, it did the job for me. I had enormous success in my attempts to teach the passive voice. My learners seemed to have understood what it was all about. From total lack of pattern recognition they moved into voluntary attempts to use the passive voice whenever it seemed plausible. We were all in a frenzy.
The affective element played of course an equally important role. They were highly motivated, not only by the new medium, which no doubt had the charm of novelty, but also by the enthusiasm of their lecturer who made it all possible for them. Wishing to explore that success and eventually get some support for my good work I applied and was funded to do some research into the matter. I observed a population of German learners, tested their performance before and after having worked with the program. I also tested the general language proficiency and retention. The attitude of students towards the medium was explored through questionnaire and interview. The research goal was unfortunately somewhat restricted to the efficacy of the program use. This kind of research orientation belongs to an early CALL era where the chief goal of research was to justify the costs and persuade the authorities to put some more money into it for the sake of enormous time saving, individualization, and adjustability. I was aware of the fact that there was more to it than just the stated and generally known facts. There was for instance an implied hypothesis on the nature of LSP and the passive voice as a true element of it in that software. The computer as a medium helped me present it in an acceptable way and immediately. I did not have to wait for new linguistic ideas to influence language teaching methodology and consequently learning materials, which would have lasted at least ten years. I could transfer my intuition directly into a powerful learning device. That was the beauty of it. Since I was to pursue my ideas about special language anyway, I decided to try and find out in what way the success of the program was connected with a linguistic hypothesis, and through that indirectly with teacher intuition.
WHAT IS LSP?
According to experts (Hoffmann, Fluck, Littmann) the research into LSP is still on the pre-scientific stage. Much research work has been done in the scope of word statistics, but all the carefully compiled corpora and sometimes even manually derived frequency lists could not replace a hypothesis on the true nature of LSP and the testing of it. The result of this state of affairs is that there is no plausible and, in the sense of formal logic, no valid definition of LSP. A valid definition has to contain certain indispensable formal and structural elements: it is a statement which subsumes the term to be defined under a whole class of terms which have something in common with it. Not just any class would do. It has to be the closest related class, the so-called genus proximum. The second constituent element of definition is the differentia specific, the specific differenceof the defined term in relation to the class, the feature that no other element of the same class possesses. Lothar Hoffmann, the most persistent explorer of LSP, came closest to fulfilling these stipulations. He defined special language as language used by specialists in a certain field of knowledge in order to communicate with their fellow specialists on the issues of their special field of knowledge. What we learn from Hoffmann is that special language belongs to the class "language." He does not tell us what its specific difference is. In that he makes it an attribute of intradisciplinary communication, he o y determines the scope of LSP, he does not tell us anything about the essence of it. What is it then that language? Everyone agrees that LSP is more than language itself, because the knowledge of language is not enough for an outsider to understand an expert text. What is it then that makes a familiar natural language appear so strange and incomprehensible. What prevents us from understanding differentiates special language from anything else that falls under the category
the message even with the aid of a dictionary? Semiotics teaches us that within culture we have to do with systems of signs. Culture itself is a an overall system including various subsystems like language, religion, commerce, art, science. Culture also implies interaction between the different systems, relations of superordination, subordination, and coordination. Sign systems have their evolutions, they live so to speak according to their own immanent laws of dialectic change; they interfere with each other, superimposing a new organization method on the already organized, which is how structures of vast complexity are called into being. That we would call multiple encoding.
From the semiotic point of view one could say that special language is a result of interference of another system of signs with the lingual code. Language as it is, with its elements, lexemes, and its rules, grammar, is simply encoded again. There is a superimposed structure, another principle of organization to govern it, and that is in the first instance that of formal logic. What happens is that the lexeme as the basic linguistic unit of meaning together with its significance — the word and its significance — the whole field of meaning is re-initiated as a significance of a new sign on a different, hierarchically higher level. The sign is called "a term" and the significant is called "definition." The cross-associative richness of the lingual sign, its mythological potential in ambiguity and vagueness is pressed into a strictly speaking binary code: genus proximum and differentia specifica, the typical and the characteristic, totally according to the logical principle of contradiction: no one thing can be both or none of two wholly exclusive contradictory terms.
Furthermore, a sentence as a language unit becomes a statement. There are strict rules about that. Not any sentence can become a statement. A question, an exclamation, an expression of hope, grief or anger cannot function as logical statements. The semantics of a logical statement is different than that of a sentence. Whereas the sentence chiefly expresses relations between certain syntactically selected lexical meanings, a statement points out to a logical value TRUE or FALSE. Logical values T/F are not immanent in natural language. In natural language nothing has either value per se. It takes an additional code, where these values are assigned to statements by convention, agreement. It takes the logical structure and the axioms of a particular science to assign a TRUE or FALSE value to a statement. As opposed to science, which is the filling of a
logical structure with content, the truth in natural language is mostly only a matter of the point of view. It gains more plausibility if there is a declaration of axioms or pseudo-axioms, but that already is a prerogative of logical code, which can interact with language outside the strictly confined limits of science. There are different theories to describe a logical statement. They all agree that a statement consists of a subject and a predicate. However, some of them are relatively
new, so they could not have had a crucial influence on scientific thought, and consequently, on special language. The predicate theory is the oldest and the most widespread statement theory. It tends to describe the predicate as an argument to the subject. In the statement "The king is old" "the king" is the subject and "is old" is the predicate. We see that the verb in the sentence which expresses the logical relations between the subject and the predicate is merely a copula. It has no lexical meaning in the given context. The predicate theory cannot really cope with the lexical verb. Research into LSP grammar has shown that there is a parallel between the structure of an LSP sentence and predicative statement. The clear tendency towards a desemantization of lexical verbs, the definition-based semantics of technical terms, as well as the rules of syllogism clearly suggest that LSP discourse encodes natural language once more, and that the superimposed structure, the secondary code, is that of formal logic.
Introducing binary logical organization into natural language does not totally eradicate ambiguity or vagueness. This has something to do with the nature of the primary code, the natural language itself, which is older than formal logic and, which still allows for analogy, contradiction, nonsense. Secondly, not all of the discourse within an LSP communication act is doubly encoded. Sometimes there are passages of pure natural language. This is only too understandable, since apart from being experts, the people involved in expert communication are still human. Besides, in an interdisciplinary environment there is sometimes a need to translate from the special language of another field of knowledge. Thirdly, there is an interference with the third code or codes, the sebeing special fields of knowledge which are referred to. Misunderstandings may arise from the variety of fields of knowledge involved. Under these circumstances it is possible that one significant, one word or lingual sign is associated with different definitions, depending on the special area involved at the time. The same happens to grammar: grammatical categories become the significant or logical
relations between the terms within a statement. The statement tends to be built in the manner of predicate theory: S(ubject) is (copula) P(redicat).= > TRUE
There are people who have a rather arbitrary objection that grammar cannot be an element of special language, that it is purely an attribute of natural language. This claim is mostly based on total inexperience with LSP and is never connected with any kind of proof. As opposed to this rather affective attitude we can say that the syntax of LSP is reincoded. It is encoded again according to statement building rules. The predicative statement type being the oldest statement type, most of the sentences in LSP discourse get reincoded as statements of predicative type with pre-agreement assigned logical values TRUE or FALSE, which according to Günther Littmann do not tell us much about objective reality, but everything about the way language is used or rather to be used. In other words, it is a prescriptive, metalingual function, and is therefore outside the natural language itself. This is where our passive voice research sets in. The implied hypothesis in my passive tutor was that passive voice in a way also becomes an element of LSP. At the time the program was designed I was maybe not able to formulate it, but it was there. This is how I would express it today: The passive voice is used to imitate the predicative structure of the most widespread statement type.The house(S) is (copula) built (P).
We tried to test the hypothesis on German as a special language for mechanical engineering. The analysis was performed on a newly compiled corpus containing 463 samples of written and spoken discourse (total 50,095 words). The corpus was compiled at the Polytechnic of Bremen thanks to a research scholarship awarded to me by DAAD (Deutsche Akademische Austauschdienst). The original plan was to have it parsed and make a sentence pattern statistics. The research goal was actually to determine the passive voice frequency and distribution in the corpus and to find out if and in what way it emulates the predicative statement type. In German there are different constructions with the passive meaning. We chose several: For organizational reasons the parsing was postponed so I had to do some pre-research work in that I analyzed the structure of main clauses in the whole corpus manually. I chose the main clauses only because they are in most cases clearly distinguishable. It would have been a time-consuming job to make distinction between clauses and phrases in a subordinate function, in addition to doing the analysis manually.
CONCLUSION
It was interesting to see that most of the active statements in the corpus had the predicative structure. As Table I shows, the use of the passive voice proved to be a conversion from sentences with lexical verbs and lexical and semantic meaning into
logical statements with predicative structure and an assigned TRUE or FALSE value. This happened with verbs whose valence demanded several actants, at least a subject and an object. Since a full lexical verb with more than one actant does not comply with the predicative statement, the language was rearranged so as to produce a logical statement with a subject term and a predicate consisting of a quasi copulaic verb and an imitated nominal part (the past participle). In cases where the passive principle was carried out by some verb other than the standard passive format, the used verb was delexicalized and desemantized, forced into the conjunctive function of a copula. Final results are yet to be obtained. The research however seems to confirm the implicit intuitive hypothesis on the basis of which a highly successful CALLware package dealing with the passive voice was developed. It provides linguistic reasons for its success in the classroom. To come back to the initial issues, teaching and learning is an
instance of one and undivided reality, which demands holistic approach, in research as well as in the application itself. There are innumerable reasons why a computer in a language class sometimes fails and sometimes succeeds. One should be very careful about generalizations. To state simply that the computer is better than a teacher in a language class would be faticous. The research goal would have to be in what cases, given what factors, the computer is really superior or applicable at all. The answers to these questions will never be final. The development of technology will dictate new educational roles for the computer. One thing, however, will remain unchanged: the need to explore the computer as an educational medium in interaction with a number of different parameters. The content itself must determine the role.

Linguistic Knowledge can Improve Information Retrieval


William A. Woods Lawrence A. Bookman Ann Houston
Robert J. Kuhns Paul Martin
Stephen Green
Sun Microsystems Laboratories
1 Network Drive
Burlington, MA 01803
fWilliam.Woods,Ann.Houston,Robert.Kuhnsg@east.sun.com
fPaul.Martin,Stephen.Greeng@east.sun.com
March 10, 2000

Abstract
This paper describes the results of some experiments using a new approach to information
access that combines techniques from natural language processing and knowledge representation with a penalty-based technique for relevance estimation and passage retrieval. Unlike
many attempts to combine natural language processing with information retrieval, these results show substantial benefit from using linguistic knowledge.
1 Introduction
An online information seeker often fails to nd what is wanted because the words used in the
request are different from the words used in the relevant material. Moreover, the searcher usually
spends a significant amount of time reading retrieved material in order to determine whether it
contains the information sought. To address these problems, a system has been developed at
Sun Microsystems Laboratories (Ambroziak and Woods, 1998) that uses techniques from natural
language processing and knowledge representation, with a technique for dynamic passage selection and scoring, to significacantly improve retrieval performance. This system is able to locate specific passages in the indexed material where the requested information appears to be, and to score those passages with a penalty-based score that is highly correlated with the likelihood that they contain relevant information. This ability, which we call \Precision Content Retrieval" is achieved Lawrence Bookman is now at Torrent Systems, Inc. by combining a system for Conceptual Indexing with an algorithm for Relaxation-Ranking Specific
Passage Retrieval.
In this paper, we show how linguistic knowledge is used to improve search e ectiveness in this
system. This is of particular interest, since many previous attempts to use linguistic knowledge
to improve information retrieval have met with little or mixed success (Fagan, 1989; Lewis and
Sparck Jones, 1996; Sparck Jones, 1998; Varile and Zampolli, 1997; Voorhees, 1993; Mandalaetal., 1999) (but see the latter for some successes as well).
2. Conceptual Indexing
The conceptual indexing and retrieval system used for these experiments automatically extracts
words and phrases from unrestricted text and organizes them into a semantic network that inte-
grates syntactic, semantic, and morphological relationships. The resulting conceptual taxonomy
(Woods, 1997) is used by a speci c passage-retrieval algorithm to deal with many paraphrase relationships and specific passages of text where the information sought is likely to occur. It
uses a lexicon containing syntactic, semantic, and morphological information about words, word
senses, and phrases to provide a base source of semantic and morphological relationships that
are used to organize the taxonomy. In addition, it uses an extensive system of knowledge-based
morphological rules and functions to analyze words that are not already in its lexicon, in order to
construct new lexical entries for previously unknown words (Woods, 2000). In addition to rules
for handling derived and inflected forms of known words, the system includes rules for lexical
compounds and rules that are capable of making reasonable guesses for totally unknown words.
A pilot version of this indexing and retrieval system, implemented in Lisp, uses a collection
of approximately 1200 knowledge-based morphological rules to extend a core lexicon of approximately 39,000 words to give coverage that exceeds that of an English lexicon of more than 80,000base forms (or 150,000 base plus inflected forms). Later versions of the conceptual indexing and retrieval system, implemented in C++, use a lexicon of approximately 150,000 word forms that is automatically generated by the Lisp-based morphological analysis from its core lexicon and an input word list. The base lexicon is extended further by an extensive name dictionary and by further morphological analysis of unknown words at indexing time. This paper will describe some experiments using several versions of this system. In particular, it will focus on the role that the linguistic knowledge sources play in its operation.
The lexicon used by the conceptual indexing system contains syntactic information that can
be used for the analysis of phrases, as well as morphological and semantic information that is
used to relate more specific concepts to more general concepts in the conceptual taxonomy. This
information is integrated into the conceptual taxonomy by considering base forms of words to subsume their derived and inflected forms (\root subsumption") and more general terms to subsume more specific terms. The system uses these relationships as the basis for inferring subsumption relationships between more general phrases and more speci c phrases according to the in tensional subsumption logic of Woods (Woods, 1991).
The largest base lexicon used by this system currently contains semantic subsumption information for something in excess of 15,000 words. This information consists of basic \kind of" and \instance of" information such as the fact that book is a kind of document and washing is a kind of cleaning. The lexicon also records morphological roots and axes for words that are derived or inflected forms of other words, and information about different word senses and their interrelationships. For example, the conceptual indexing system is able to categorize becomes black as a kind of color change because becomes is an inflected form of become, become is a kind of change and black is a color. Similarly, color disruption is recognized as a kind of color change, because the system recognizes disruption as a derived form of disrupt, which is known in the lexicon to be a kind of damage, which is known to be a kind of change.
3 Relaxation Ranking and Specific Passage Retrieval
The system we are evaluating uses a technique called \relaxation ranking" to specific passages
where as many as possible of the different elements of a query occur near each other, preferably
in the same form and word order and preferably closer together. Such passages are ranked by a
penalty score that measures the degree of deviation from an exact match of the requested phrase,
with smaller penalties being preferred. Differences in morphological form and formal subsumption of index terms by query terms introduce small penalties, while intervening words, unexplained permutations of word order, and crossing sentence boundaries introduce more significant penalties. Elements of a query that cannot be found nearby introduce substantial penalties that depend on the syntactic categories of the missing words.
When the conceptual indexing system is presented with a query, the relaxation-ranking retrieval
algorithm searches through the conceptual taxonomy for appropriately related concepts and uses
the positions of those concepts in the indexed material to specific passages that are likely to
address the information needs of the request. This search can find relationships from base forms
of words to derived forms and from more general terms to more specific terms, by following paths in the conceptual taxonomy.
For example, the following is a passage retrieved by this system, when applied to the UNIX                operating system online documentation (the \man pages"):
Query: print a message from the mail tool 6. -2.84 print mail tool
Print sends copies of all the selected mail items to your default printer. If there are no selected items, mail tool sends copies of those items you are currently. The indicated passage is ranked6th in a returned list of found passages, indicated by the 6in the above display. The number -2.84 is the penalty score assigned to the passage, and the subsequent words print, mail, mail, and mail tool indicate the words in the text that are matched to the corresponding content words in the input query. In this case, print is matched to print, message to mail, mail to mail, and tool to mail tool, respectively. This is followed by the content of the actual passage located. The information provided in these hit displays gives the information seeker a clear idea of why the passage was retrieved and enables the searcher to quickly skip down
the hit list with little time spent looking at irrelevant passages. In this case, it was easy to identify
that the 6th ranked hit was the best one and contained the relevant information.
The retrieval of this passage involved use of a semantic subsumption relationship to match
message to mail, because the lexical entry for mail recorded that it was a kind of message. It used
a morphological root subsumption to match tool to mail tool because the morphological analyzer
analyzed the unknown word mail tool as a compound of mail and tool and recorded that its root
was tool and that it was a kind of tool modified by mail. Taking away the ability to morphologically analyze unknown words would have blocked the retrieval of this passage, as  would eliminating the lexical subsumption entry that recorded mail as a kind of message.
Like other approaches to passage retrieval (Kaszkiel and Zobel, 1997; Salton et al., 1993; Callan, 1994), the relaxation-ranking retrieval algorithm identifies relevant passages rather than simply
identifying whole documents. However, unlike approaches that involve segmenting the material
into paragraphs or other small passages before indexing, this algorithm dynamically constructs
relevant passages in response to requests. When responding to a request, it uses information in
the index about positions of concepts in the text to identify relevant passages. In response to a
single request, identified passages may range in size from a single word or phrase to several sentences or paragraphs, depending on how much context is required to capture the various elements of the request.
In a user interface to the specific passage retrieval system, retrieved passages are reported to
the user in increasing order of penalty, together with the rank number, penalty score, information
about which target terms match the corresponding query terms, and the content of the identified
passage with some surrounding context as illustrated above. In one version of this technology,
results are presented in a hypertext interface that allows the user to click on any of the presented
items to see that passage in its entire context in the source document. In addition, the user can
be presented with a display of portions of the conceptual taxonomy related to the terms in the
request. This frequently reveals useful generalizations of the request that would find additional
relevant information, and it also conveys an understanding of what concepts have been found in
the material that will be matched by the query terms. For example, in one experiment, searching
the online documentation for the Emacs text editor, the request jump to end of le resulted in
feedback showing that jump was classified as a kind of move in the conceptual taxonomy. This
led to a reformulated request, move to end of le, which successfully retrieved the passage go to
end of buffer.

4 Experimental Evaluation
In order to evaluate the effectiveness of the above techniques, a set of 90 queries was collected
from a naive user of the UNIX operating system, 84 of which could be answered from the online
documentation known as the man pages. A set of \correct" answers for each of these 84 queries
was manually determined by an independent UNIX operating system expert, and a snapshot of the man pages collection was captured and indexed for retrieval. In order to compare this methodology with classical document retrieval techniques, we assign a ranking score to each document equal to the ranking score of the best ranked passage that it contains.
In rating the performance of a given method, we compute average recall and precision values
at 10 retrieved documents, and we also compute a \success rate" which is simply the percentage of queries for which an acceptable answer occurs in the top ten hits. The success rate is the principal factor on which we base our evaluations, since for this application, the user is not interested in subsequent answers once an acceptable answer has been found, and finding one answer for each of two requests is a substantially better result than finding two answers to one request and none for another.
These experiments were conducted using an experimental retrieval system that combined a
Lisp-based language processing stage with a C++ implementation of a conceptual indexer. The
linguistic knowledge sources used in these experiments included a core lexicon of approximately
18,000 words, a substantial set of morphological rules, and specialized morphological algorithms
covering inflections, prefixes, suffixes, lexical compounding, and a variety of special forms, including numbers, ordinals, Roman numerals, dates, phone numbers, and acronyms. In addition, they made use of lexical subsumption taxonomy of approximately 3000 lexical subsumption relations, and a small set of semantic entailment axioms (e.g., display entails see, but is not a kind of see). This system is described in (Woods, 1997). The database was a snapshot of the local man pages (frozen at the time of the experiment so that it wouldn't change during the experiment), consisting of approximately 1800 les of varying lengths and constituting a total of approximately 10 megabytes.
5 Discussion
Table 1 shows that for this task, the relaxation-ranking passage retrieval algorithm without its supplementary knowledge sources (Recall II w/o knowledge) is roughly comparable in performance (42.9% versus 44.0% success rate) to a state-of-the-art commercial search engine (SearchIt) at the pure document retrieval task (neglecting the added benefit of locating the specific passages). Adding the knowledge in the core lexicon (which includes morphological relationships, semantic subsumption axioms, and entailment relationships), but without morphological analysis of un-
known words (Recall II w/o morph), significantly improves these results (from 42.9% to 50.0%).
Further adding the morphological analysis capability that automatically analyzes unknown words
(deriving additional morphological relationships and some semantic subsumption relationships)
significantly improves that result (from 50.0% to 60.7%). In contrast, we found that adding the
same semantic subsumption relationships to the commercial search engine, using its provided thesaurus capability degraded its results, and results were still degraded when we added only those facts that we knew would help find relevant documents. It turned out that the additional relevant documents found was more than o set by additional irrelevant documents that were also ranked more highly.

6.  Anecdotal Evaluation of Specific Passage Retrieval Benefits
As mentioned above, comparing the relaxation-ranking algorithm with document retrieval systems measures only a part of the benefit of the specific passage retrieval methodology. Fully evaluating the quality and ranking of the retrieved passages involves a great many subtleties. However, two informal evaluations have been conducted that shed some light on the benefits.
The rest of these was a pilot study of the technology at a telecommunications company. In
that study, one user found that she could use a single query to the conceptual indexing system to find both of the items of information necessary to complete a task that formerly required searching two separate databases. The conclusion of that study was that the concept retrieval technology performs well enough to be useful to a person talking live with a customer. It was observed that the returned hits can be compared with one another easily and quickly by eye, and attention is taken directly to the relevant content of a large document. The automatic indexing was considered a plus compared with manual methods of content indexing. It was observed that an area of great potential may be in a form of knowledge management that involves organizing and providing intelligent access to small, unrelated \nuggets" of textual knowledge that are not amenable to conventional database archival or categorization.
A second experiment was conducted by the Human Resources Webmaster of a high-tech company, an experienced user of search engines who used this technology to index his company's internal HR web site. He then measured the time it took him to process 15 typical HR requests first using conventional search tools that he had available, and then using the Conceptual Indexing technology. In both cases, he measured the time it took him to either nd the answer or to conclude that the answer wasn't in the indexed material. His measured times for the total suite were 55 minutes using the conventional tools and 11 minutes using the conceptual indexing technology. Of course, this was an uncontrolled experiment, and there is some potential that information learned from searching with the traditional tools (which were apparently used first) might have provided some benefit when using the conceptual indexing technology. However, the fact that he found things with the latter that he did not nd with the former and the magnitude of the time difference suggests that there is an effect, albeit perhaps not as great as the measurements. As a
result of this experience, he concluded that he would expect many users to take much longer to find materials or give up, when using the traditional tools. He anticipated that after finding some
initial materials, more time would be required, as users would end up having to call people for
additional information. He estimated that users could spend up to an hour trying to get the information they needed...having to call someone, wait to make contact and finally get the information
they needed. Using the conceptual indexing search engine, he expected that these times would be
at least halved.
Conclusion
We have described some experiments using linguistic knowledge in an information retrieval system in which passages within texts are dynamically found in response to a query and are scored and ranked based on a relaxation of constraints. This is a different approach from previous methods of passage retrieval and from previous attempts to use linguistic knowledge in information retrieval. These experiments show that linguistic knowledge can significantly improve information retrieval performance when incorporated into a knowledge-based relaxation-ranking algorithm for specific passage retrieval.
The linguistic knowledge considered here includes the use of morphological relationships between words, taxonomic relationships between concepts, and general semantic entailment relationships between words and concepts. We have shown that the combination of these three
knowledge sources can significantly improve performance in finding appropriate answers to specific
queries when incorporated into a relaxation-ranking algorithm. It appears that the penalty-based
relaxation-ranking algorithm figures crucially in this success, since the addition of such linguistic knowledge to traditional information retrieval models typically degrades retrieval performance rather than improving it, a pattern that was borne out in our own experiments.

Thursday, December 23, 2010

Linguistic reality and technological limitations - brains vs chips


Thomas Hansen
University of Southern Denmark
Natural Interactive Systems Lab
thomas@nis.sdu.dk

Abstract

Behind the somewhat pompous name of this paper hides a much
more modest aim. It is an attempt at clarifying one of the major
problem areas found in the field of Computer Assisted Language
Learning (CALL), specifically the use of Automatic Speech
Recognition (ASR). The aim of CALL is to produce tools aiding
the acquisition of foreign languages and, long-term, to produce
full-fledged, virtual, multi-modal language tutors. In essence, the
teacher in the machine.
Focusing specifically on Computer Assisted Pronunciation
Training (CAPT) I intend to present a, hopefully, pedagogic
picture of how the mind works in relation to adopting the sounds of
a foreign language (L2), both perception- and productionwise, and
illustrate the shortcomings of ASR in comparison.
I also intend to present you with some rather unsubstantiated ideas
on language learning which can hopefully generate a wholesome
debate.
1. Introduction - and some fairly unsubstantiated claims open for discussion
Learning a foreign language is by no means a task which is accomplished easily. In recent years a trend has become visible which, within school systems, exposes children to foreign languages
earlier than ever seen before and the immigration politics of different countries focus much more on adult immigrants acquiring the native language as a prerequisite for permanent residence.
As the demand for faster, better and more autonomous language study increases, scientists have
begun looking at the computer as a potential aid in language acquisition. Some even speculate in the idea of an autonomous interactive, multi-modal language teacher/tutor. Although I find myself convinced that this will someday be reality, there are, as yet, multiple areas in which technology needs to improve in order to qualify for such a label, ASR not the least.
In my opinion the area in which computers can, presently and long-term, benefit learners the most is within the area of pronunciation. Very often in classes consisting of 20-40 people teachers will not have the available time to practice with and correct individual student pronunciation. Areas such as vocabulary and grammar tutoring lend themselves much more easily to a (blackboard) class-based teaching environment. In my opinion languages are not learned in class. Only building blocks aiding the learning of the language are provided within such an environment. One does not fully learn a language until one has to use the language in an actual situation with native speakers.
Furthermore, using a language entails possessing some rudimentary communication skills. It is my firm belief that three different priorities for language acquisition can be established1:
1. Vocabulary
2. Pronunciation
3. Grammar and all the rest.
 This claim was partly reached through a conversation with a Chinese test subject who said, “I know everything about
the grammar, I just can’t for the life of me pronounce it.”
In order to be able to speak a language a learner must know some basic words. Without a
vocabulary nothing else will work. Again, in order to use that vocabulary a learner needs to be able to pronounce the contents. Subsequently learners can be taught to arrange words in their proper
position. Whether a student utters the sentence: 1) “Me beer want,” or 2) “I would like a beer,” he or she will still be understood. This is comparable to tourists visiting foreign countries and availing themselves of standard phrases which have been picked up in one way or the other.
Learning a language in class is much like learning to drive a car. Initially you are presented with all the rules and interpretations of the signs, but it is not until you find yourself alone, driving the car on your own in actual traffic that you really learn how to drive.
Computer Assisted Language Learning, in time, carries the potential to provide you with your own car, but there are many hurdles which need to be overcome before this becomes reality.
2. Computer Assisted Language Learning
Computer Assisted Language Learning (CALL) and Computer Assisted Pronunciation Training
(CAPT) are areas in the academic field that explore the role of information and communication
technologies in language learning and teaching. This includes: materials development, pedagogical practice and research. Both are interrelated areas that have experienced rapid growth in recent years.Especially the introduction of the internet and the computer as a household item has spawned the possibility of online learning and computer applications in language learning [Levy, 1997].
Today CALL/CAPT have established themselves as prolific areas whose advantages are well
known to language educators. As a matter of fact I believe that this area should be viewed as a branching tree with a headline and several subdivisions, depending on your focus. CALL seems to me to be initial node, CAPT comes directly beneath it on its own branch. Other branches could be areas of grammar learning and vocabulary learning.
 See for instance (Neri, Cucchiarini, Strik & Strik 2002).
1) Training available at any time the learner wishes – (install on laptop and transport)
2) More and faster individualized feedback for the learner. – (only the learner uses it)
3) Stress free environment for the learner to practice in. – (nobody listens, no time limit)
4) More practice time than in a classroom setting. – (available anytime)
The above four points should be viewed in terms of a class setting versus a non-class setting.
But, as pointed out by Neri [Neri, 2001], many are concerned with the missing link between
technological advances and Second Language Acquisition (SLA) research. One task at hand lies in bridging the gap between proceduralists and formalists, those who wish to build applications versus those with knowledge of language [Chambers, 2001]. In essence a method for pronunciation training is needed which focuses on both the linguistic perception and production facets of learning a language as well as taking into consideration the shortcomings and possibilities of present day technology.
Perception and production
Nearly all people are born with the capacity of speaking one language with native competence (L1), but children and adults differ greatly in their ability to learn a second language (L2). To find bilingual children with the competence of speaking both languages with native qualities is not unusual and even throughout their initial years of education they may acquire a third or fourth language where they attain near-native qualities. Research shows that around the age of thirteen the capacity for learning a language seems to diminish, otherwise known as the Critical Period
Hypothesis (CPH) [Lenneberg, 1967; Flege, 1987]. Upon reaching adolescence our capacity to
interpret new phonetic input (i.e. sounds not indigenous to our native language) seems to fossilize. This is evidenced in many adults attempting to acquire a second language but producing it with heavy accent coloring. In 1957 Noam Chomsky coined the term Universal Grammar (UG) [Chomsky, 1957] arguing that all people possessed an underlying language module which acted as a blueprint for acquiring language. Once all the sounds of the native language have been ‘put into place,’ they are grouped in acoustic categories appropriate for the L1.5 Heavy accent coloring in L2 acquisition has then hitherto been explained via Equivalence Classification [Flege, 1987]: Upon attempting to acquire the sounds of the L2, these pass through the ‘phonological filter’ of the L1 and are assimilated to the sounds already known. In other words, the learner will attempt to compare the new sound with an already existing one in his or her native language, and produce the already known sound rather than the new one. This is also known as Acoustic Assimilation. Hence, a necessary component of speaking a foreign language with native quality is the ability to perceive the phonetic differences between the mother tongue and the target language. Additionally the ability to produce the sound is dependent on the ability that a learner must be able to perceive the difference in acoustic quality before he/she can produce it and thereby make room for new acoustic categories for the language in question.6
There are, of course, numerous other processes involved in the acquisition of a foreign language
besides the ones that relate specifically to speech viewed as a simple acoustic signal. These relate to extra-linguistic factors such as body movement, but are not treated in this paper.
It should be noted that the theory of Universal Grammar is highly controversial and opposing viewpoints can be found in most of the linguistic literature by J.R. Firth and M.A.K. Halliday. I am not an avid proponent of UG theory, but it does seem to provide a pedagogically sound picture of the elements involved in language acquisition. Acoustic categories can here be thought of as those ‘vowels’ and ‘consonants’ that we view as a fixed inventory for a particular language. Other languages will posses different acoustic qualities.
There are opposing views on the matter such as Borrel [Borrel, 1990] who claims that production precedes perception. Mastering the motor mechanics (in relation to the tongue), should facilitate the perception factor. My personal view, based mostly on simple intuition, disagrees with that statement.
4 CALL – ASR and all the problems we encounter. Today the most widely used piece of technology in CAPT applications are Automatic Speech
Recognition (ASR) systems. ASR allows the L2 learner to interact with the constructed software as well as provides the possibility of the learner’s pronunciation to be understood and evaluated
immediately, by analyzing the acoustic signal provided by the learner. In an ideal CAPT application the level of feedback should emulate that provided by human teachers in terms of, for instance, segmental correction. A teacher standing in front of a student is able to offer corrective feedback in respect to motor movements of the mouth, articulatory advice, guide the student on the phonetic as well as the sentence level. Hence, a non-native speaker pronouncing the word as should be informed that the second segment should be pronounced differently, followed by exemplification. Currently the best available option in terms of computer feedback is provided through the use of ASR systems. However, numerous problems are encountered when employing ASR technology in CAPT systems. These problems are manifold and can be exemplified on, at
least, two separate, yet interdependent, levels:
1. Single word pronunciation
2. Sentence level pronunciation
ASR systems, unlike the human auditory system, are currently incapable of distinguishing between and non-speech sounds. Hence a cough, a sigh, a hiccup, the slamming of a door, the chirp of a bird, all these sounds will be interpreted by the ASR system as constituting a speech signal and the system will attempt to match this signal with an already incorporated vocabulary. Even the possibility of having a faint echo in the room when producing an utterance will potentially be misinterpreted by the ASR.
Limited vocabulary, or simply out-of-vocabulary, constitutes another problem for ASR systems.
Most often the training material available to the student is fairly limited since not all words,
counting inflections and similar, are part of the trained or inbuilt vocabulary. This in turn brings up a related problem, namely that of dialectal variation and speaking style. Dialects can in some cases vary to such a degree that they can almost be considered languages within languages. Even the human auditory system can run into trouble at this level. Differences are also visible in terms of physiological differences in the human vocal tract.
In addition to dialects, we also encounter differences in speaking styles as well as differences
between the acoustic signals of men, women and also children. In many cases ASR systems are not capable of handling these differences unless specifically designed to cater to a particular group.
Furthermore ASR systems often run into trouble when the vocabulary becomes too similar. Hence, minimal pairs such as and are very difficult to distinguish. Most ASR systems seem happier when the phonetic material provided is polysyllabic. Even more difficult, if not entirely impossible is the aspect of getting ASR systems to provide segmental feedback. Segmental feedback requires the isolation and identification of single-phones in a context of other phones. Hence the system should ideally be able to recognize whether a [k] is pronounced with aspiration or without aspiration, or even as non-released. ASR systems are composed, mostly, of diphones or triphones, hence eliminating the possibility of single-phone identification. Creating an acoustic model which would be able to distinguish between all the different phonetic realizations of sounds does indeed provide a daunting task.
The already mentioned problems are then even further magnified when examining continuous speech. Continuous speech has no apparent boundaries from which the ASR system can solidly deduce single words. This can be seen from perhaps the most widely used example listed below.
How is an ASR system to know whether a speaker says:
1. Recognize speech
2. Wreck a nice beach
As human listeners we have the advantage of context and reasoning, an ASR system does not.
Further examples can be found in the area of homophones, which are words that sound the same but are spelled differently i.e. sale versus sail. All in all speech recognition still suffer from severe shortcomings in terms of functioning as substitute and autonomous language teachers.
5 Conclusion
In this paper I have attempted to outline two very different processes, which aim at achieving the
same goal, namely that of creating new speech sounds in learners of a foreign language, perception and production of the mind versus perception in ASR.
As we have seen, acquiring new speech sounds is by no means an easy task once a native language has become fossilized. Human teachers have the ability to tutor and monitor individuals as well as correcting minuscule differences between the mother tongue and the L2, but to some degree they suffer from not having enough time to devote to each individual students. ASR, if viewed as part of a larger package, does contain the possibility of providing students with more time for learning and individual practice and correction, but are simply not able to generate feedback which is finegrainedenough to highlight small differences in language sounds. Furthermore, ASR systems as standalone applications are entirely unable to provide the learner with the perception part of language learning. ASR does currently provide the best option in terms of generating feedback, but we are a long way away from creating alternatives to real-life teachers.

Applied linguistics


Main article: Applied linguistics
Whereas theoretical linguistics is concerned with finding and describing generalities both within particular languages and among all languages, applied linguistics takes these results and applies them to other areas. Often applied linguistics refers to the use of linguistic research in language teaching, but this is just one sub-discipline:
• Research in language teaching: today, 'applied linguistics' is sometimes used to refer to 'second language acquisition', but these are distinct fields, in that SLA involves more theoretical study of the system of language, whereas applied linguistics concerns itself more with teaching and learning. In their approach to the study of learning, applied linguists have increasingly devised their own theories and methodologies, such as the shift towards studying the learner rather than the system of language itself, in contrast to the emphasis within SLA.[13][14]
• Applied computational linguistics: two computer applications are speech synthesis and speech recognition, which use phonetic and phonemic knowledge to provide voice interfaces to computers. Machine translation, computer-assisted translation, and natural language processing are fruitful areas which have also come to the forefront in recent years.
• Clinical linguistics entails the application of linguistics to speech-language pathology. This involves treating individuals whose linguistic development is atypical or impaired.[15] This branch of applied linguistics may also involve treatment of specific language impairment, where one aspect of language develops exceptionally.[16] The field has also adopted existing ideas which have have not become 'mainstream' in theoretical linguistics. For example, both behaviourism[17] and natural phonology[18] have appeared in the literature.

Article: Linguistic-No Easy Answers in Evolution of Human Language

March 9, 2008 
Linguistics Anthropology is the study of human language since that is a characteristic that makes humans unique. This article shows how bird songs in some birds are now helping in gaining a better understanding of the development of human language. This brings together biologic, culture, archaeological, and linguistics together with science to learn more about the complex social interaction of human language and communication. ENJOY, I did and learn about new bread discoveries in linguistic anthropology.
ScienceDaily (Feb. 21, 2008) — The evolution of human speech was far more complex than is implied by some recent attempts to link it to a specific gene, says Robert Berwick, professor of computational linguistics at MIT.
Berwick will describe his ideas about language in a session at the annual meeting of the American Association for the Advancement of Science on Feb. 17. The session is called “Mind of a Toolmaker,” and explores the use of evolutionary research in understanding human abilities. Some researchers in recent years have speculated that mutations in a gene called Foxp2 might have played a fundamental role in the evolution of human language. That was based on research showing that the gene seems to be connected to language ability because some mutations to that gene produce specific impairments to language use, and because our closest living relatives, the chimpanzees, lack both these gene mutations and the capacity for language. But the claim that the gene mutation is directly connected to the development of language is very unlikely to be right, says Berwick, who holds appointments in MIT’s Department of Electrical Engineering and Computer Science and Department of Brain and Cognitive Sciences. “This kind of straightforward connection is just not the way organisms are put together,” he says. When it comes to something as complex as language, “one would be hard-pressed to come up with an example less amenable to evolutionary study.” And the specific Foxp2 connection is based on a whole chain of events, each of which is speculative, so there’s little chance of the whole story being right. “It’s so chaotic, it’s like weather forecasting,” he says. “The noise overwhelms the signal”. Rather, language is almost certainly the result of a far more complex and subtle interplay among a variety of factors, Berwick says, and it may never be possible to connect it to specific genetic changes. “There are some things in science that are very interesting, but that we’re never going to be able to find out about,” he says. “It’s a sort of romantic view some people have, that anything interesting can be understood.”
Even defining something as complicated as language in a precise way is daunting, as ongoing disputes over the significance of language experiments with apes, parrots and dolphins have made clear. Berwick says, “If you can’t define what it is, why study it from an evolutionary point of view?”
It’s more likely, Berwick says, that the role of the Foxp2 gene in language is somehow peripheral to the capacity for language itself. He compares it to a printer in a computer system–its part of the overall system, but it’s not fundamental. Berwick thinks a more productive approach to studying the evolution of language is to examine it in terms of deeper, internal mechanisms. In his own research, Berwick has compared the structure of languages with the structure of bird songs, and has found interesting connections that may lead to a better understanding of some aspects of language.
Both bird songs and all human languages seem to share some underlying characteristics related to their metrical structure, Berwick s ays. There’s an underlying sing-song beat that is pronounced in poetry, music and in the songs of birds that may reveal a fundamental aspect of how our brains process language. Future research could probe this link further, even looking at possible connections between other specific genes, in both birds and humans that might be connected to this sense of metrical structure. Ultimately, the important thing is to understand that language is, at bottom, something that takes place inside the human mind and is independent of any particular sound, sight or motion. The same internal mental construction could be expressed through verbal speech, through writing or through sign language without changing its basic nature, Berwick says. “It’s not about this external thing you hear,” he says. “It’s about the representation inside your head.”
Adapted from materials provided by Massachusetts Institute of Technology

Maximizing Vocabulary Development by Systematically Using a Depth of Lexical Processing Taxonomy, CALL Resources, and Effective Strategies



JOHN PAUL LOUCKY
Seinan Women’s University
Abstract:
How can one better use modern CALL resources to help language learners to build up a larger target language (TL) vocabulary quickly? A major need that quickly becomes apparent at the intersection of the fields of second language vocabulary acquisition (SLVA) and computer-assisted language learning (CALL) is the lack of sufficient research with respect to their most advantageous integration for language learning. The depth of lexical processing taxonomy presented here seeks to integrate the research studies that exist into a clear and effective approach that uses insights and innovations of both fields to create a user-friendly system for more rapid vocabulary acquisition and activation. Because of the proliferation of language-learning programs and websites, it becomes important to help define which most essential vocabulary and reading strategies should be included in those programs and websites. When designing programs and websites, the three major parameters of subjective enjoyment, objective effectiveness, and technological efficiency should all be considered. This article compares various studies to suggest how many of the features and functions of CALL tools can be used at the most appropriate stages of lexical processing and acquisition to make vocabulary learning smoother, quicker, and more effective. It helps to fill a noticeable gap by providing a clear, step-by-step taxonomy of 40 vocabulary learning strategies (VLSs), simplified into an eight-fold scale of major cognitive phases for ease of instruction and use in classes. These strategies are channeled through a logical depth of lexical processing (DLP) scale, which can help promote both learners receptive and productive vocabulary development, whether using traditional text-based or CALL-enhanced methods. The ultimate goal is to find practical ways to use a research-based DLP scale and VLS taxonomy to improve the teaching and monitoring of essential phases of vocabulary learning, either in traditional classes or in CALL learning environments.
INTRODUCTION
Establishing a comprehensive and effective taxonomy of vocabulary-learning strategies useful in a CALL environment has been a research area in much need of investigation (Duquette, Renié, Laurier, 1998; Loucky, 2003c). Although many foreign language learners appear to use only a limited number of language- and lexical-learning strategies, as do most of the Japanese college students surveyed in the study presented here, teachers who wish to offer more effective instruction are looking for some system that can combine as many proven strategies, good ideas, and resources as possible to help their students more quickly improve their vocabulary knowledge, reading comprehension, and language-learning skills. This article compares various vocabulary acquisition studies and suggests how many of the functions of intelligent computer-assisted language learning (ICALL) can be used at each of the appropriate stages of lexical processing and acquisition to make the process smoother, quicker, and more effective. As such, the article helps to fill a gap by providing a clear, step-by-step VLS taxonomy divided into 10 major cognitive phases. These strategies are channeled through a logical DLP scale designed to promote learners' receptive and productive vocabulary development. The specific goal of this study is to further develop Schmitt's (1997) vocabulary learning taxonomy in ways that would make it simultaneously more teachable to students, more useful in monitoring and assessing their use of specific strategies, and more applicable to either text-based or CALL-based language learning environments. Kudo (1999) and Orita (2003) completed what seems to be an adequate replication of Schmitt's (1997) taxonomy, using several distinct groups of Japanese students at different levels of proficiency, but the students' levels of proficiency were determined only by the number of years of English study.
In earlier studies, this author has suggested a taxonomy of vocabulary acquisition which postulates that effective lexical processing of new vocabulary may be done by using a cyclical series of open-ended phases for assessing, accessing, archiving, analyzing, anchoring, associating, activating, anticipating, reassessing, and relearning/remeeting new terms (Loucky, 2003c). As new TL terms are processed and recycled through these various phases, automaticity of word recognition is developed, and, eventually, the item is retained. In itself, this cyclical process does not prove anything, but it does illustrate current theory on lexical processing strategies and provides a simple, research-based framework for more effective CALL program design and classroom instruction. The author has compared the use of many types of media and methods for enhancing the vocabulary learning of Japanese college students, investigating the relative benefits of using monolingual, bilingual, and fully bilingualized (both L1 and L2 glosses) dictionaries (Loucky, 1996, 2004a, 2005b). He has also researched the use of computerized dictionaries versus more traditional textbook or paper dictionaries for improving English lexical development and reading comprehension (Loucky, 2002a, 2002b, 2003a, 2003b, 2004a, 2005b). Finally, he compared the effectiveness of the so-called “mnemonic keyword” versus the “semantic field keyword method” (Loucky, 2004b) since the latter approach had never been developed either bilingually or tested to its full potential when used in a CALL environment. As part of that on-going study, the author undertook a study of the relationships between various patterns of strategy use, electronic dictionary use, and subsequent vocabulary growth, culminating in the DLP scale and VLS taxonomy discussed here.
LITERATURE REVIEW
Few online vocabulary development or reading courses have been created that take into account current CALL practice, SLA theory, and the practical needs and proficiency levels of language learners. The interplay among learners' language proficiency, computer proficiency, and academic needs based on their majors must be more carefully considered.
Chapelle (2001), who outlined the foundations for electronically enhanced teaching, testing, and research, noted six major criteria to consider in determining whether a CALL task is appropriate for a given language-learning situation:
1. language learning potential
2. learner fit,
3. meaning focus,
4. authenticity,
5. impact, and
6. practicality.
Chapelle provided several tables with excellent guiding questions for logically evaluating CALL tasks (see especially her Tables 3.4-3.7). The practical questions that Chapelle raised can help CALL practitioners and developers to better design and evaluate programs by focusing their attention on the six major criteria above. Both vocabulary and comprehension components of reading should be evaluated, as well as opportunities for more integrated language skill development. Well balanced and holistic language development can be encouraged either by giving students more individual computerized interaction in multimedia formats or by providing socially oriented language-learning experiences in which students are asked to apply new learning more productively. Joe (1995, 1998) showed that one of the types of learning behaviors strongly associated with promoting L2 vocabulary acquisition is generation, requiring students to generate language forms as in a “pushed output production” condition (see Swain, 1985, 1995; de la Fuente, 2002). Vocabulary learning activities that promote attention, such as the use of a vocabulary-knowledge-scale assessment (see below), recall or retrieval, and generative use in original, creative ways by students can surely help to foster more rapid second language acquisition. Chen (2004) stated that many
CALL researchers worldwide are interested in developing and using reading materials/environments to enhance second language learners' language competence … in the effect of multimedia annotation modes on reading comprehension and vocabulary acquisition (e.g. Chun & Plass, 1997; Seghayer, 2001) … in screening and arranging of authentic texts by controlling vocabulary items (Ghardirian, 2002) … in the use of electronic dictionaries or online glossing in reading processes (e.g. Lomicka, 1997; Roby, 1998; Laufer & Hill, 1999). (p. 51) However, few CALL reading specialists like Chen have concentrated on the practical task of providing a supportive reading environment for both language teachers and language learners, especially online environments (however, see http://www.CALL4ALL.us, http://CALL4ALL.us///home//_all.php?fi=r) While many CALL-related articles have mainly focused on technological issues, Shield and Kukulska-Hume (2004) have affirmed that “Pedagogical and intercultural aspects of usability are often overlooked, and as a result, the contribution that content providers [teachers, researchers and web designers] could make towards the evaluation of their own web sites is not even considered” (p. 27). The arguments presented here take these factors into consideration since it is well known that higher levels of student enjoyment and engagement improve learning motivation and results.
Gallo-Crail and Zerwekh (2002) examined how 20 students of Tagalog at Northern Illinois University “used different learning strategies with different Web-based tools as they studied new vocabulary words and how this affected their success in learning and mastering the new vocabulary. … This case study describes student VLS use of five different learning strategies that were supported by Internet-based activities” (p. 56). Gallo-Crail and Zerwekh measured student achievement on quizzes and collected data on learners' VLS preferences in five kinds of vocabulary-learning activities: (a) association by word matching: a memory strategy, (b) translation by flashcard exercise: a cognitive strategy, (c) using linguistic clues via a movable online dictionary browser with sound: a compensatory strategy, (d) cultural background knowledge via weekly culture themes online: an affective strategy, and (e) reviewing and linking known to new vocabulary: a metacognitive strategy via online preview cards using sound and charts to introduce new words coming in the next weekly theme. Students made weekly study reports on accountability charts, participated in interviews and online strategy surveys, and completed online tests. Among Gallo-Crail and Zerwekh's findings most relevant to this study are the following. Learning efforts that combine “a semantic processing strategy and a keyword strategy … promote more vocabulary acquisition than a keyword or semantic mapping alone” (p. 56). The researchers also determined that learners familiar with and using a “wider variety of strategies achieved greater success in learning new vocabulary words than those who did not” (p. 65). The present study found this same pattern of relationships between higher VLS usage and higher vocabulary retention and use.
THE BASIS FOR THE PROPOSED TAXONOMY IN THE LITERATURE
It should be noted that depth of processing does not exclude the use of translation pairs as one of the initial methods of accessing and confirming new TL meanings. A translation pair has a solid memory connection and the benefit of immediate recognition, so that at early stages of language learning word-pair learning, even in decontextualized lists, can be an effective way to increase receptive vocabulary. Nevertheless, something must be done with new TL terms in order for them to be adequately assimilated into the learner's L2 mental lexicon, as well as transferred from merely passive recognition to more active recall and productive use.
Contextualized use of language by learners generating their own productive expressions seems to be required for more thorough assimilation and retention. For new TL vocabulary to move into productive use and long-term memory, it seems that some form of negotiation of meaning must take place using actual language in tasks requiring productive output of some kind. Swain (1985) and de la Fuente (2002) have called these teacher-initiated production tasks “pushed output.” Two recent studies (Loucky 2004a, 2004b) have shown very good results for both lower level and higher level proficiency language learners when pushed output tasks are blended with CALL-enhanced bilingual glosses added to arrays of related word groups using Crow's semantic field keyword approach (Crow & Quigley, 1985; Crow, 1986a, 1986b).
Different vocabulary-learning strategies seem to be more appropriate at different levels of learning and proficiency (e.g., initial learning strategies vs. subsequent consolidating strategies). Learners at beginning levels seem to benefit more from the use of bilingual dictionaries and word pairs. More advanced language learners, however, benefit from the use of both L1 and L2 tools (see Laufer & Hadar, 1997) because they are ready to process L2 explanations and use the L2 expressively. Both conventional wisdom and research studies have suggested that there are important links between language learners' vocabulary knowledge and literacy skills and their academic and career achievements. For example, Wells (1985) found that the foundation of good literacy skills is laid in the home and that good learning experiences at school can help to reinforce those skills. He concluded that the “major determinant of education achievement is the extent of a child's mastery of literacy” (p. 193). Nation (2001) also provided a system for analyzing vocabulary-learning strategies, one that the current study has adopted in a modified form. Nation's three major categories of vocabulary strategies are
1. planning and choosing what to focus on,
(This is interpreted here as meaning what teachers should focus on and help learners to focus on because many lower proficiency language learners do not know what their initial focus or processing steps in vocabulary learning should be.)
2. finding sources of information about vocabulary, and
(This is interpreted here as accessing and elaborating new word meanings.)
3. processes for establishing vocabulary knowledge.
(This is interpreted here as consolidating vocabulary knowledge.)
Sanaoui (1992, 1995) compared both ESL and EFL students and found clear distinctions between structured versus unstructured learners, noting that these two groups of learners differed in five important ways:
1. learners' opportunities for vocabulary learning (i.e., independent study vs. reliance on course [and teacher] direction),
2. learners' range of self-initiated vocabulary learning (i.e., extensive vs. restricted ranges),
3. learners' records of lexical items they were learning (i.e., extensive/systematic vs. minimal/ad hoc),
4. how much learners reviewed their lexical records (i.e., extensive vs. little or none), and
5. whether learners practiced lexical items (creating opportunities for use in and out of class vs. relying on class instruction only).
Other studies have also concluded that electronic dictionaries were superior to print dictionaries mainly based on reduced search time (e.g., Taylor & Chan, 1994; Koga, 1995; Inami, Nishikata, Nakayama, & Shimizu, 1997; Loucky, 2002a, 2002b, 2003b, 2003c).
Clearly, the strategic choice between dictionary use and other VLS options is an important skill for L2 readers to develop. So far, however, few studies have considered how CALL could help to raise awareness and practice in using a much broader range of effective and essential VLSs. The present study addresses this question.
THE STUDY
There are as yet few studies examining what specific kinds of vocabulary learning strategies L2 students employ when using CALL materials and how the use of these strategies may differ in quality or quantity from those used when reading print materials. To contribute towards an understanding of online reading and vocabulary development, the author developed a taxonomy of vocabulary learning strategies based on Schmitt's (1997) useful distinction between discovery versus consolidating strategies. This 40-item VLS taxonomy gives brief descriptions of strategies used at the discovery and consolidating stages of vocabulary development (see the modified version of Schmitt's survey in Appendix A and the results of its use in Table 3 below). Besides studies investigating the effects of hypermedia on vocabulary development, (e.g. Akbulut, 2004a, 2004b; Coll, 2002), few detailed CALL or electronic-dictionary-based studies of VLS use have been done, with the exception of the studies by Hill and Laufer (2003) and Kobayashi (2005).
Optimal conditions governing strategy use in traditional versus CALL environments need to be further developed using various computer functions and online features to encourage learners to include essential phases of lexical processing. The author undertook the present study to better assess Japanese students' use of VLSs based on the modified version of Schmitt's survey and a DLP checklist derived from a combination of these VLSs and an eight-phase DLP scale.
METHOD
Procedures and Participants
The project involved 112 students in five groups distributed over a 5-year period of time. The students in groups 1-4 were Japanese freshmen engineering students at a national university in Kyushu between 2000 and 2004. Group 5 consisted of engineering graduate students enrolled in a one-semester-only course at the same university. While most Japanese students have completed 6 years of English study at the secondary level before entering the university, the students in this project averaged 7.32 years of English study, including after school conversation and cram school English training. Table 1 lists the number students in each group and their vocabulary grade level, as assessed by a US-normed standardized reading test (Gates McGinite). The author has found consistent results in using these levels to estimate Japanese high school and college students' reading and vocabulary levels over two decades (Loucky, 1996, 1997a, 2002c, 2003a).
Based on the results of this pilot study, Schmitt's original survey, the modified 40-item VLS taxonomy form, and the short VLS checklist (Part C in Tables 2 and 3) were administered to students in groups 2-5 in the following years. Students in each group were given instructions in a teacher-made workbook on how to monitor their use of VLSs and were encouraged to use as many of them as possible in the lexical processing stages listed in the tables above whenever they encountered new words during their readings. They completed the VLS taxonomy during the academic year and the short VLS checklist at the end of the year. Students were asked to total and compute the percentage of their VLS use (2.5 points per VLS used X 40 = 100%) and then to take the shorter DLP checklist in Part C in Table 2 and summarize their overall use of its eight phases (x/8 actively used versus x/8 thought to be helpful).
RESULTS
The responses of students in Groups 2-4 (N = 58) are reported in aggregate form in Table 4 representing the VLSs that the students used most often or found most helpful. In the cells in Table 4, the first number (e.g., 44 Ls) refers to the number of learners who reported using that strategy or finding it helpful. The second number (e.g., SS VLS 8) refers to the strategy number in Schmitt's survey. The final number (e.g., DT 7, meaning item 7 on the author's DLP taxonomy questionnaire) refers to the comparable strategy found in the author's VLS taxonomy, based on its DLP scale grid number. The results presented in Table 4 mirror many of findings of Schmitt (1997), Kudo (1999), and Orita (2003), among others. Schmitt and Orita found that the most frequently used VLSs for discovery were guessing from context, using a bilingual dictionary, and asking classmates. Likewise in this study, these two VLSs were the most highly preferred strategies for learning new words. The preferred VLSs were, in rank order, (a) using bilingual dictionaries (VLS 8 used by 44 learners), (b) guessing from context (VLS 13 used by 36 learners), and (c) using monolingual dictionaries (VLS 7 used by 35 learners). For consolidation, the preferred VLSs were (a) studying new words often over time (VLS 27 used by 22 learners) and writing new words out (VLS 26 used by 19 learners).
DISCUSSION
In this study, most of these Japanese college students showed a low average use of VLSs, except for students in the most advanced class (the graduate students in Group 5). Despite having had an average of 7.32 years of previous study in English, only about half of the students (52%) reported using even the first three of the eight stages of vocabulary learning. Although three fourths of the students (74%) thought that each of the eight types of VLSs would be useful in their language learning, their average use of all eight steps was only 37%.
Students in the lower proficiency level classes used, on average, only 26% of the 40 VLSs. The most widely used strategies were accessing unknown words in Japanese (62%, compared to only 20% for accessing unknown words in English), asking classmates for help or using an English-Japanese book dictionary (50%), archiving/recording meanings of unknown words or simply skipping over unknown words (45%), trying to guess meanings from context (40%), using monolingual dictionaries (37%), and using English to Japanese electronic dictionaries (35%, compared with 25% for using Japanese to English electronic dictionaries). The other VLSs were used by under 30% of the lower proficiency level students.
The graduate students in group 5 used a slightly higher percentage of VLSs than the lower proficiency undergraduate students, 32% versus 26%. However, the most proficient undergraduate students (Group 3), who had the highest average use of electronic dictionaries, also had the highest average use of VLSs (41%). The students in this group also had an average vocabulary gain of 1.07 over the academic year, the highest vocabulary gain observed by the researcher in the past decade at this institution. Students who used electronic dictionaries consistently and diligently generally reached higher levels of English proficiency than those who did not (Loucky, 2005c). A more detailed analysis of dictionary use by the students in Group 3 revealed that 11 out of 21 learners surveyed (52%) used electronic dictionaries (2 bilingual only and 9 fully bilingualized—with both bilingual and monolingual lexicons), whereas 6 students (29%) used only bilingual print dictionaries. Four other students (19%) did not bring a dictionary to class; they each borrowed a monolingual learner's dictionary of their choice from a class collection. Some students carried both print and electronic dictionaries to class but tended to use their electronic devices for easier, more rapid access to unknown words. These results seem to indicate that systematic use of electronic dictionaries can help foreign language learners to focus more time and attention on essential VLSs, especially regularly reassessing, recycling and reusing new terms. This is because quickly accessing new word information and automatically saving it for review are two of the major advantages of portable electronic dictionaries.
Schmitt (1997), Kudo (1999), Orita (2003), and the findings of this study show that most Japanese students, regardless of the number of years of English language learning, use relatively few strategies when learning new TL vocabulary, suggesting that these students have not had effective training in VLSs. Few secondary English teachers in Japan seem to be aware of VLS taxonomies to help students learn more effective strategies for discovering the meaning of unknown TL vocabulary or for consolidating their knowledge of TL vocabulary.
While about half of all students reported using the first three types of VLSs, discovery strategies following Schmitt's model, less than one third of the students (28%) made an effort to use any of the other five types VLSs. This means that 72% of the students failed to use more cognitively or communicatively demanding processes, namely any of those VLSs characterized by lexical phases 5-8 in the DLP scale. Only the most advanced students, who routinely used electronic dictionaries, reported using the greatest number of VLSs.
More research should also be done on the beneficial language-learning effects of using various CALL reading programs (see Chen, 2004; Loucky, 2003d, 2003e) along with consistent and well guided use of computerized bilingual and/or monolingual dictionaries (see Loucky, 2002a, 2002b, 2003b, 2003c), especially when these dictionaries are systematically used to enhance the most essential lexical-processing steps elucidated by the DLP scale.
PEDAGOGICAL IMPLICATIONS
Even though a taxonomy of essential vocabulary processing strategies can be easily taught, as the author did with the students in Group 1, effective vocabulary learning strategies must be practiced and actively used to become regular, independent language-learning habits. One can apply Sanaoui's (1992, 1995) distinction between “structured” versus “unstructured” to evaluate learners' approach to vocabulary learning since their approach clearly affects the resulting degree of lexical learning.
PEDAGOGICAL IMPLICATIONS
Even though a taxonomy of essential vocabulary processing strategies can be easily taught, as the author did with the students in Group 1, effective vocabulary learning strategies must be practiced and actively used to become regular, independent language-learning habits.
Constructing an Ideal CALL Environment
Based on the considerations discussed above, the specific learning factors should be clearly distinguished for better analysis of their respective roles in second-language acquisition (SLA) in general and second-language-vocabulary acquisition (SLVA) in particular when developers construct an electronic text or CALL program. The following factors can serve as useful guidelines for constructing an ideal CALL environment to maximize SLA and SLVA:
1. inclusion of learning style preferences (often due to the how individuals have learned their first language and its linguistic characteristics, e.g., Kanji versus alphabetic orthography),
2. vocabulary learning strategies (e.g., those required by the teacher/text or imposed by the CALL program vs. those chosen by learners),
3. means of glossing (e.g., L1 translations, L2 definitions only, or fully bilingualized combinations of both L1 and L2 information, perhaps including full sentence examples of major meanings [Laufer & Hadar, 1997; Loucky 2004b] and still picture or video annotations),
4. language-learning tasks or factors included in language-learning tasks (e.g., quantity, richness, and degree of organization of new TL vocabulary),
5. depth of lexical processing or more elaborative cognitive processing (see Craik & Tulving, 1975),
6. attractiveness (e.g., quality of learner interface and ease of use),
7. degree and type of language interaction (e.g., interaction required or prompted by the program),
8. availability of technical functions (e.g., pacing of voice, text screens, types of semantic and syntactic elaboration, and multimedia links),
9. degree of learner motivation, interest, and purpose (i.e., how these are encouraged in the program), and
10. cost effectiveness (e.g., students' preference for free online or downloadable programs, see examples at http://www.CALL4ALL.us).
Strategies Instruction
The use of both a DLP scale and a VLS taxonomy acquisition model would enable teachers to focus on training their students to use effective strategies to develop fluency in the TL. These instructional strategies should clearly include teaching students how to
1. assess their vocabulary knowledge and learning by means of some kind of vocabulary knowledge scale such as the one proposed by Loucky (2002a, 2005a) (see also Loucky, 2003b, 2004d; Wesche & Paribakht, 1996),
2. use intelligent, integrated CALL tools such as computerized bilingual dictionaries of various sorts, to enhance each phase of lexical processing (e.g., rapidly accessing new word meanings and recording lexical information in an organized way),
3. use a DLP scale such as that proposed here to guide them to greater variety and depth of both word and text processing (Craik & Tulving, 1975),
4. use CALL software and online programs that include a greater number of multimedia features as well as more lexical-processing steps (see Takefuta, 1999),
5. identify and focus on high-frequency, common-core vocabulary (Nation & Newton, 1997; Loucky, 2002c) and academic words (Coxhead, 2000),
6. maximize active practice of TL terms in productive development and communicative interactions in computer-mediated or face-to-face sessions (Loucky, 1997b, 1998; Hatch & Brown, 1995; Nation, 2001; Thornbury, 2002), and
7. systematically organize new TL vocabulary to build up a broad web of associative networks by means of a semantic field keyword approach (Crow, 1986; Quigley, 1986; Loucky, 2004a, 2004b, see also http://www.CALL4ALL.us/SFKAINDEX.htm).
In sum, language learners at an early acquisition level should focus on phonetic-word decoding and sight-word learning and, at later intermediate and advanced stages, on developing those strategies most needed for discovering new meanings from context based on predictions from known words. A consistent finding from observing the students in this project is that the higher a class's average vocabulary level is, the greater the number of students who use electronic dictionaries and CALL resources to improve their learning (Loucky, 2002a, 2002b, 2003b, 2003c, 2004a, 2004b). From the early intermediate level, language learners should be trained in how to effectively use more rapid-access computerized tools and instant online glossing, starting at word and phrase levels with L1 translations and moving on to sentence and discourse levels of complexity with L2 glosses. Consistent use of VLS taxonomies like those reviewed here and systems that encourage (a) greater depth of lexical processing, (b) wider breadth of syntactic complexity, and (c) repeated encounters with new TL forms and meanings in as many different contexts as possible can often be significantly facilitated by the rapid access provided by portable and online CALL dictionaries and translation software (Saunders, 2002; Coll, 2002; see also http://www.call4all.us///home/_all.php?fi=d).
CONCLUSIONS
The 40-item VLS taxonomy organized by phases in the DLP scale presented here consolidates Schmitt's (1997) original vocabulary-learning-strategies taxonomy, while, at the same time, adding both the semantic field keyword approach and the use of portable electronic or online dictionaries (distinguishing bilingual from fully bilingualized dictionaries). The DLP scale not only preserves the helpful distinction between initial discovery and subsequent consolidating strategies, but also adds a useful analytical grid of the major phases of lexical processing to help clarify SLVA processes.
The author's review of the limited number of VLS classification systems available—each with different parameters and means of organization—led him to the conclusion that a descriptive, learner-centered approach has not helped to provide a sufficiently clear and complete system that can be easily grasped in order to be taught, learned, and used for actual language learning and research. Thus, this VLS taxonomy and DLP scale sought to combine insights from previous studies to help language teachers and language learners maximize vocabulary development. Koyama and Takeuchi (2004, p. 41) are correct in stating that “With regard to foreign language acquisition … task-induced involvement might be more important than look-up frequency (Laufer & Hulstijn, 2001). Look-up frequency, thus, does not necessarily result in better learning.” Although Koyama and Takeuchi claimed that “words searched in a longer process in PD [print dictionary] condition could be retained better than those in ED condition” (p. 42)—supposedly supporting the depth of processing hypothesis—merely increasing learners' time on task does not necessarily ensure deeper or more elaborative cognitive processing.
We should examine not only the amount of time on task and number of words accessed, but also the quality of lexical processing being employed by the systematic use of more thorough elaborative vocabulary learning strategies. A comparison of such studies leads us to conclude that dictionaries, whether print or electronic, are merely tools whose features and functions require training for effective use. Further, language learners have different profiles, needs, and preferences, so that various kinds of dictionaries and various kinds of information presented in dictionaries have different benefits for learners at different levels of proficiency. While print picture dictionaries are often best for younger, beginning-level learners, older learners with computer skills may be far more motivated to use CALL-based lexical resources. Deeper lexical processing seems to result in better retention, but only if other essential learning phases and strategies are included such as archiving, anchoring, and activating. These findings show where CALL can help
learners by providing more specific focus and elaboration on new word forms, meanings, and appropriate usage in each phase of vocabulary development.