1997 ExpansionOfMultiWordTerms

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Subject Headings: Controlled Terms Index Production Task, Terminology Extraction Task.

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Abstract

1 Motivation

  • Terms are known to be excellent descriptors of the informational content of textual documents (Srinivasan, 1996), but they are subject to numerous linguistic variations. Terms cannot be retrieved properly with coarse text simplification techniques (e.g. stemming); their identification requires precise and efficient NLP techniques. We have developed a domain independent system for automatic term recognition from unrestricted text. The system presented in this paper takes as input a list of controlled terms and a corpus; it detects and marks occurrences of term variants within the corpus. The system takes as input a precompiled (automatically or manually) term list, and transforms it dynamically into a more complete term list by adding automatically generated variants. This method extends the limits of term extraction as currently practiced in the IR community: it takes into account multiple morphological and syntactic ways linguistic concepts are expressed within language. Our approach is a unique hybrid in allowing the use of manually produced precompiled data as input, combined with fully automatic computational methods for generating term expansions. Our results indicate that we can expand term variations at least 30% within a scientific corpus.

2 Background and Introduction

  • NLP techniques have been applied to extraction of information from corpora for tasks such as free indexing (extraction of descriptors from corpora), (Metzler and Haas, 1989; Schwarz, 1990; Sheridan and Smeaton, 1992; Strzalkowski, 1996), term acquisition (Smadja and McKeown, 1991; Bourigault, 1993; Justeson and Katz, 1995; Dalle, 1996), or extraction of linguistic information e.g. support verbs (Grefenstette and Teufel, 1995), and event structure of verbs (Klavans and Chodorow, 1992).
  • Although useful, these approaches suffer from two weaknesses which we address. First is the issue of filtering term lists; this has been dealt with by constraints on processing and by post-processing overgenerated lists. Second is the problem of difficulties in identifying related terms across parts of speech. We address these limitations through the use of controlled indexing, that is, indexing with reference to previously available authoritative terms lists, such as (NLM, 1995). Our approach is fully automatic, but permits effective combination of available resources (such as thesauri) with language processing technology, i.e., morphology, part-of-speech tagging, and syntactic analysis.
  • Automatic controlled indexing is a more difficult task than it may seem at first glance:
    • controlled indexing on single-words must account for polysemy and word disambiguation (Krovetz and Croft, 1992; Klavans, 1995).
    • controlled indexing on multi-word terms must consider the numerous forms of term variations (Dunham, Pacak, and Pratt, 1978; Sparck Jones and Tait, 1984; Jacquemin, 1996).
  • We focus here on the multi-word task. Our system exploits a morphological processor and a transformation-based parser for the extraction of multi-word controlled indexes.
  • The action of the system is twofold. First, a corpus is enriched by tagging each word unambiguously, and then expanded by linking each word with all its possible derivatives. For example, for English, the word genes is tagged as a plural noun and morphologically connected to genic, genetic, genome, genotoxic, genetically, etc. Second, the term list is dynamically expanded through syntactic transformations which allow the retrieval of term variants. For example, genic expressions, genes were expressed, expression of this gene, etc. are extracted as variants of gene expression.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1997 ExpansionOfMultiWordTermsChristian Jacquemin
Judith Klavans
Evelyne Tzoukermann		Expansion of Multi-Word Terms for Indexing and Retrieval Using Morphology and Syntaxhttp://www.aclweb.org/anthology-new/P/P97/P97-1004.pdf10.3115/976909.979621
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