Such lists are useful in establishing vocabulary learning goals, assessing vocabulary knowledge and growth, analyzing text difficulty and richness, creating and modifying reading materials, designing vocabulary learning tools, determining the vocabulary components of academic curricula, and fulfilling many other crucial academic needs cf. Nation and Webb Because so much is currently based on pedagogical word lists, it is crucial that the words in any academic list be truly representative of contemporary academic language, and that they be identified using sound methodological principles—which brings us to our present study involving a new Academic Vocabulary List AVL.
During the s, pioneering scholars in the area of vocabulary produced several lists of general academic words based on various small corpora of academic materials, primarily consisting of textbooks Campion and Elley ; Praninskas ; Lynn ; Ghadessy Because of limited computing power at the time, these lists were compiled by hand.
Some were based on basic frequency and range criteria Campion and Elley ; Praninskas , while others were based on student annotations of words they did not understand in their textbooks Lynn ; Ghadessy These user-friendly programs were produced and freely distributed by Nation and his colleagues. However, the need for a more representative academic list was expressed by Coxhead in a seminal article describing her Academic Word List AWL : … as an amalgam of the four different studies, it [the UWL] lacked consistent selection principles and had many of the weaknesses of the prior work.
The corpora on which the studies were based were small and did not contain a wide and balanced range of topics. Recently, the AWL has also received a great deal of attention in primary and secondary education, particularly in the USA e. Hiebert and Lubliner ; Baumann and Graves ; Nagy and Townsend , where concern continues regarding the widening gap between high and low academic achievers. However, all of this interest in the AWL has also resulted in more careful scrutiny of the methodology behind the list, with several concerns being consistently pointed out in the literature.
We will briefly address the two that appear to be most problematic: the use of word families to determine word frequencies, and the relationship of the AWL with the General Service List GSL; West The AWL was determined by using word families, with a word family being defined as a stem headword plus all inflections and transparent derivations containing that stem Coxhead For example, the word family react contains the following members:.
The choice to base text coverage on word families has been criticized on several levels. First, members of an extensive word family like react may not share the same core meaning c.
Nagy and Townsend Consider, for example, the differences in primary meanings between react respond , reactionary strongly opposed to social or political change , reactivation to make something happen again , and reactor a device or apparatus. These meaning differences are accentuated further as members of word families cross over the various academic disciplines Hyland and Tse We have added the parts of speech for discussion purposes.
Using lemmas would also take care of three additional problems with the proceed family above: i the noun proceedings meaning records or minutes would be correctly counted on its own, ii the noun procedure meaning technique and its inflected plural form, procedures , would be correctly grouped and counted together on their own; and iii the adjective procedural meaning technical or routine would be correctly counted on its own.
However, in a word-family approach, all of these word forms with their variant meanings and grammatical functions would be counted together as a single word family.aborprosgo.gq
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Another major concern with counting word families instead of lemmas to produce pedagogical word lists is that knowledge of derivational word relationships comes much later than knowledge of inflectional word relationships for most school-aged children and second language adults see Gardner , for review. In short, it is also clear from these learning perspectives that lemmas inflectional relationships only should be preferred to word families inflectional and derivational relationships in determining pedagogical word lists, especially if those lists are intended to be used by learners at less than advanced English proficiency cf.
Schmitt and Zimmerman It is important to remember that these COCA lemma groupings inflections only are not nearly as extensive as word family groupings inflections plus derivations , thus making the overlap even more noteworthy. These findings with COCA American English , along with the findings of the BNC studies cited in the first paragraph of this section British English , provide strong evidence that i the AWL is largely a subset of the high-frequency words of English and should therefore not be thought of as an appendage to the GSL, and ii the GSL, as a whole, is no longer an accurate reflection of high-frequency English.
Regarding the first point, we draw attention to the fact that the AWL produces good coverage of academic materials precisely because it does contain so many high-frequency words. We have no problem with this fact, only with the way that the GSL—AWL relationship has been explained for purposes of instructional vocabulary sequencing and vocabulary-coverage research in academic contexts.
The counter-side of this problem is that there are many high-frequency academic words in the GSL that were not considered in the AWL Nagy and Townsend ; Neufeld et al. For instance, words like company , interest , business , market , account , capital , exchange , and rate all occur in the GSL and were therefore not considered in the AWL counts, even though such words have major academic meanings. In short, because the GSL words were excluded from the AWL analysis, there is no easy way to separate the high-frequency academic words in the GSL from the high-frequency words that tend to be important in other areas of focus.
These include: i word families that are common in fiction, but not in academic text e. We could make similar lists for other non-academic genres, and there are hundreds of such non-academic word families in the GSL. As a preparatory stage for focused academic vocabulary training, sifting through such a list of general English vocabulary reflecting many genres of English, not just academic is simply inefficient.
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It is important to note that some experts have begun to question whether a core academic list such as the AWL or our proposed list is a viable notion at all. In our view, the first criticism involving meaning-variation could be applied to any high-frequency list of English that is based on forms of words, and on different disciplines of the language Gardner In fact, the more general the high-frequency list, the more problems there will be with word-meaning variation because the highest frequency content words of English are the most polysemous Ravin and Leacock The question is, until we develop a highly accurate computer program for tagging lexemes in electronic text word forms and their distinct meanings , should we throw out generalized word lists altogether?
Any well-conceived list of high-coverage words brings some order to what otherwise would be vocabulary chaos Where do we start? What can our learners focus on now, next, etc.? Until we have an accurate lexeme tagger, we can minimize to some degree the meaning problems by counting lemmas instead of word families, by teaching learners how to deal with multiple meanings, and by providing useful application tools for words on our lists. Regarding the second issue of no useful distinction between academic and general high-frequency words, we would counter with three points.
First, recent research suggests that general academic vocabulary knowledge does make a significant additional contribution to academic achievement Townsend et al. While we acknowledge the effort to describe the true place of the AWL in terms of its relationship to general high-frequency words, we do not view the amalgamated BNL as the answer for academic settings because it takes us back to the notion of a general list only, in which core academic words are stirred back into the same old polysemous word soup, where 2, or 3, general high-frequency word families could actually mean something along the lines of 20,—50, lexemes words with their variant meanings —numbers that would discourage even the most ambitious learners and teachers in academic settings.
Again, if the goal is academic English, the statistics can and do point to a narrower list of core academic words that can be focused on by learners, teachers, and researchers. The rest of the sublists were derived from general corpora. In fact, we are puzzled why the BNL developers and Coxhead as well did not take advantage of the readily available academic subcorpus of the BNC, consisting of roughly 16 million words plus many more potential academic sources in the technical magazines of the BNC.
Our preliminary analyses support this assertion. Based on our review of the literature and our own research, we believe that a new list of academic core words must consist of the following characteristics:. The new list must initially be determined by using lemmas, not word families. Subsequent groupings of the list into families may be warranted for certain instructional and research purposes. The new list must be based on a large and representative corpus of academic English, covering many important academic disciplines.
The new list must be statistically derived using both frequency and dispersion statistics from a large and balanced corpus consisting of both academic and non-academic materials. The corpus must be large enough and the statistics powerful enough to be able to separate academic core words those that appear in the vast majority of the various academic disciplines from general high - frequency words those that appear with roughly equal and high frequency across all major registers of the larger corpus, including the academic register , as well as from academic technical words those that appear in a narrow range of academic disciplines.
The academic materials in the larger corpus, as well as the non-academic materials to which it will be compared, must represent contemporary English, not dated materials from 20 to years ago. Otherwise, the validity of the new list could be questioned. The new list must be tested against both academic and non-academic corpora, or corpus-derived lists, to determine its validity and reliability as a list of core academic words. In this section, we will first discuss the corpus that was used as the basis of our AVL.
Our academic corpus is both significantly larger and more recent than the corpus that was used for the AWL Coxhead The AWL is composed of 3. It is nearly evenly divided into four disciplines—arts, commerce, law, and science—with roughly , words in each discipline. All of the texts are from the early s to the late s. In other words, our corpus is nearly 35 times larger than the AWL corpus. All texts in our academic corpus were published in the USA, and are representative of written academic materials. Like the corpus used for the AWL, there are no samples of spoken academic language in our corpus, and we acknowledge that some variation in our findings could result from this limitation.
The corpus is composed of the nine disciplines displayed in Table 2. As indicated, 85 million of the million words come from academic journals. Originally, this was the entire academic corpus for our vocabulary list.
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This conference is concerned with digital libraries. What would be an effective definition of "vocabulary" in the context of digital libraries? To answer this question we consider first the ordinary meanings of the word and then the nature of digital libraries.
The Oxford English Dictionary , vol 19, provides four definitions of "Vocabulary. A collection or list of words with brief explanations of their meanings. The range of a language of a particular person, class, profession, or the like. The sum or aggregate of words composing a language; and 4. Figuratively , A set of artistic or stylistic forms, techniques, movements, etc, available to a particular person, etc. The underlying notion is that "vocabulary" denotes an enumeration of the different expressions of meaning, the repertoire of representational forms.
In linguistics the words "type" and "token" are used, where every instance of a word is a "token," and each different kind of word is a "type. So using "vocabulary" for the range or repertoire of index terms or subject headings would be appropriate. But in Library and Information Science the terms used to express meaning are often either rather unnatural adaptations of natural language e.
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God -- Knowableness Indeed such systems for the representation of meaning are a specialty of the field. The use of such descriptive systems is a kind of language activity and they have long been referred to as "documentary languages" or "metalanguages," meaning, if you will, languages of metadata. It would, therefore, be very appropriate in Library and Information Science to extend the use of "vocabulary" to refer to the range or repertoire of allowed terms in a thesaurus, of numbers used in a classification scheme, and of codes in any categorization. In the specialized context of digital libraries, there seems no reason not to use "vocabulary" as a technical term to denote the range or repertoire of any MARC field or other metadata field.
Treating "vocabulary" a technical term in Library and Information Science to denote the range of any metadata field opens up additional possibilities, because the entire structure of digital library systems can be represented in terms sets or collections and transitions from a set to a derived set. There appear to be only two kinds of transition:. Transformation, as when a set of catalog cards are derived from a set of books, or when a set of vectors are derived from digital texts; and 2. Partitioning or re- ordering , as when cards are alphabetized or a subset of records are selected as a retrieved set.
So far as Christian Plaunt and I have been able to determine, all digital library structures, indeed all filtering and retrieval systems, can be modeled in this way by sequences of sets and transitions to derived sets. Digital library structures are hierarchies of sets: There are networks of repositories, each containing collections of documents, typically containing paragraphs composed of words made up from letters.
Metadata are composed of fields, often containing sub-fields, and so on. What is relevant here is that if digital libraries can be usefully regarded in terms of sets and if, in defining our terms in Library and Information Science, "vocabulary" could well be defined as the range of any set. If it were, then "vocabulary" immediately becomes a central technical term in this field. In information retrieval "vocabulary" usually refers to the stylized adaptation of natural language to form indexing terms.
Closer examination reveals vocabulary as a powerful and pervasive notion, because digital libraries include a multiplicity of languages and, therefore, of vocabularies. Each transaction in the familiar library catalog involves at least five different vocabularies: authors', indexers', syndetic structure, searchers', and formulated queries. Indexing, whether with "natural" or artificial notation, is a describing activity and, therefore, a language activity.
It is traditional and appropriate to refer to metadata systems as "documentary languages. If we take the term "vocabulary" in its ordinary sense to denote that range or repertoire of different words used, then it would also seem reasonable to use it for the range of any kind of metadata, for example any MARC field. If a word is to be used as a technical term in any domain, for example in Library and Information Science, it had better have an agreed meaning in that domain.
It would be reasonable and useful to use "vocabulary" to denote the range found in any set or collection of words, including all metadata, and this provides great generality when used in relation to the functional model we have proposed. Mapping across vocabularies, from a term in one vocabulary to the corresponding term or terms in another, is increasingly needed as convenient access expands to more and more repositories and to additional, less familiar metadata.
Vocabulary is central to the economics of digital libraries because unfamiliar terminology impedes effective searching. Vocabulary is also important because it is central to issues of identity, which, in turn, are central to society.
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Vocabulary, if given a technical definition in Library and Information Science as the variety or range of values in a set, is a central feature in the structure and use of digital libraries. There is another consideration. It is a simplification, but I suggest that the historical development of conceptions of Library and Information Science can be better understood if we think in terms of two different traditions, which I call a "document tradition" and a "formal tradition.
In the "formal tradition" I include all those techniques and technologies based on logic and algorithms: punch cards, digital computers, data-processing, computing, artificial intelligence, and historic traditions of information retrieval as reflected in meetings of ACM SIGIR. It is this formal tradition that has done so much to make our conference topic--digital libraries--possible.
But this tradition depends on definitions and reliable procedures and is at odds with the variability of human language and of human behavior. In the "document tradition" I would place the historic practices of document services, such as bibliography, librarianship, archivists and records managers. In this tradition the concern has been with documents in the sense of signifying objects and their use in the service of multiple objectives: practical utility, education, recreation, literacy, and diverse social services.
This tradition has a certain logic: It entails that professional practice extends to any kind of signifying object in any format, that it include potentially anything that helps knowledge, and an understanding that documents have to do with knowledge, meaning, learning, description, language, and ambiguity Buckland It follows that every conception of Library and Information Science cannot be complete if it does not incorporate cultural studies, and that, ultimately, a mature, well-developed conception of Library and Information Science must necessarily have lively roots in the concerns of the humanities and qualitative social sciences.
Two traditions appear to be, ultimately, incompatible because they start from fundamentally different bases. Nevertheless, we cannot choose either one exclusively if we are to be both effective and practical. However, vocabulary is central to both traditions. Both must, in their differing ways, deal with issues of vocabulary, which provides a kind of meeting place. The topic of vocabulary, I conclude, is important for this conference because the nature and role of vocabulary is central to any credible conception of Library and Information Science. Berman, S. Jefferson, NC: McFarland. First published by Scarecrow Press.
Buckland, M. What is a "document"? Journal of the American Society for Information Science 48 , Reprinted in T. Buckland, eds. Historical Studies in Information Science. Medford, NJ: Information Today, On the construction of selection systems. Library Hi Tech , 48 , Norgard, B. Berger, M. Teaching of the language is reinforced through:. By enrolling in a General Education language course you are declaring that you have no previous knowledge or study in the language.
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