A Nonparametric Method for Extraction of Candidate Phrasal Terms

Proceedings of the 43rd Annual Meeting of the ACL, pages 605–613, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics A Nonparametric Method for Extraction of Candidate Phrasal Terms Paul Deane Center for Assessment, Design and Scoring Educational Testing Service pdeane@ets.org Abstract This paper introduces a new method for identifying candidate phrasal terms (also known as multiword units) which applies a nonparametric, rank-based heuristic measure. Evaluation of this measure, the mutual rank ratio metric, shows that it produces better results than standard statistical measures when applied to this task. 1 Introduction The ordinary vocabulary of a language like English contains thousands of phrasal terms -- multiword lexical units including compound nouns, technical terms, idioms, and fixed collocations. The exact number of phrasal terms is difficult to determine, as new ones are coined regularly, and it is sometimes difficult to determine whether a phrase is a fixed term or a regular, compositional expression. Accurate identification of phrasal terms is important in a variety of contexts, including natural language parsing, question answering systems, information retrieval systems, among others. Insofar as phrasal terms function as lexical units, their component words tend to cooccur more often, to resist substitution or paraphrase, to follow fixed syntactic patterns, and to display some degree of semantic noncompositionality (Manning, 1999:183-186). However, none of these characteristics are amenable to a simple algorithmic interpretation. It is true that various term extraction systems have been developed, such as Xtract (Smadja 1993), Termight (Dagan & Church 1994), and TERMS (Justeson & Katz 1995) among others (cf. Daille 1996, Jacquemin & Tzoukermann 1994, Jacquemin, Klavans, & Toukermann 1997, Boguraev & Kennedy 1999, Lin 2001). Such systems typically rely on a combination of linguistic knowledge and statistical association measures. Grammatical patterns, such as adjective-noun or noun-noun sequences are selected then ranked statistically, and the resulting ranked list is either used directly or submitted for manual filtering. The linguistic filters used in typical term extraction systems have no obvious connection with the criteria that linguists would argue define a phrasal term (noncompositionality, fixed order, nonsubstitutability, etc.). They function, instead, to reduce the number of a priori improbable terms and thus improve precision. The association measure does the actual work of distinguishing between terms and plausible nonterms. A variety of methods have been applied, ranging from simple frequency (Justeson & Katz 1995), modified frequency measures such as c-values (Frantzi, Anadiou & Mima 2000, Maynard & Anadiou 2000) and standard statistical significance tests such as the t-test, the chi-squared test, and log- likelihood (Church and Hanks 1990, Dunning 1993), and information-based methods, e.g. pointwise mutual information (Church & Hanks 1990). Several studies of the performance of lexical association metrics suggest significant room for improvement, but also variability among tasks. One series of studies (Krenn 1998, 2000; Evert & Krenn 2001, Krenn & Evert 2001; also see Evert 2004) focused on the use of association metrics to identify the best candidates in particular grammatical constructions, such as adjective-noun pairs or verb plus prepositional phrase constructions, and compared the …

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