Selection of Effective Contextual Information for Automatic Synonym Acquisition

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 353–360, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Selection of Effective Contextual Information for Automatic Synonym Acquisition Masato Hagiwara, Yasuhiro Ogawa, and Katsuhiko Toyama Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya, JAPAN 464-8603 {hagiwara, yasuhiro, toyama}@kl.i.is.nagoya-u.ac.jp Abstract Various methods have been proposed for automatic synonym acquisition, as syn- onyms are one of the most fundamen- tal lexical knowledge. Whereas many methods are based on contextual clues of words, little attention has been paid to what kind of categories of contex- tual information are useful for the pur- pose. This study has experimentally inves- tigated the impact of contextual informa- tion selection, by extracting three kinds of word relationships from corpora: depen- dency, sentence co-occurrence, and prox- imity. The evaluation result shows that while dependency and proximity perform relatively well by themselves, combina- tion of two or more kinds of contextual in- formation gives more stable performance. We’ve further investigated useful selection of dependency relations and modification categories, and it is found that modifi- cation has the greatest contribution, even greater than the widely adopted subject- object combination. 1 Introduction Lexical knowledge is one of the most important re- sources in natural language applications, making it almost indispensable for higher levels of syntacti- cal and semantic processing. Among many kinds of lexical relations, synonyms are especially use- ful ones, having broad range of applications such as query expansion technique in information re- trieval and automatic thesaurus construction. Various methods (Hindle, 1990; Lin, 1998; Hagiwara et al., 2005) have been proposed for syn- onym acquisition. Most of the acquisition meth- ods are based on distributional hypothesis (Har- ris, 1985), which states that semantically similar words share similar contexts, and it has been ex- perimentally shown considerably plausible. However, whereas many methods which adopt the hypothesis are based on contextual clues con- cerning words, and there has been much consid- eration on the language models such as Latent Semantic Indexing (Deerwester et al., 1990) and Probabilistic LSI (Hofmann, 1999) and synonym acquisition method, almost no attention has been paid to what kind of categories of contextual infor- mation, or their combinations, are useful for word featuring in terms of synonym acquisition. For example, Hindle (1990) used co- occurrences between verbs and their subjects and objects, and proposed a similarity metric based on mutual information, but no exploration concerning the effectiveness of other kinds of word relationship is provided, although it is extendable to any kinds of contextual information. Lin (1998) also proposed an information theory- based similarity metric, using a broad-coverage parser and extracting wider range of grammatical relationship including modifications, but he didn’t further investigate what kind of relationships actually had important contributions to acquisi- tion, either. The selection of useful contextual information is considered to have a critical impact on the performance of synonym acquisition. This is an independent problem from the choice of language model or acquisition method, and …

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