Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar

Knowledge-Rich Approach to Automatic Grammatical Information Acquisition: Enriching Chinese Sketch Engine with a Lexical Grammar Chu-Ren Huang Inst. Of Linguistics Academia Sinica churen@sinica.edu.tw Wei-Yun Ma Inst. Of Info. Science Academia Sinica ma@iis.sinica.edu.tw Yi-Ching Wu Inst. Of Linguistics Academia Sinica d898702@oz.nthu.edu.tw Chih-Ming Chiu Inst. Of Linguistics Academia Sinica henning@hp.iis.sinica.edu.tw Abstract This paper discusses the implementation of a knowledge-rich approach to automatic acquisition of grammatical information. Our study is based on Word Sketch Engine (Kilgarriff and Tudgell 2002). The original claims of WSE are two folded: that linguistic generalizations can be automatically extracted from a corpus with simple collocation information provided that the corpus is large enough; and that such a methodology is easily adaptable for a new language. Our work on Chinese Sketch Engine attests to the claim the WSE is adaptable for a new language. More critically, we show that the quality of grammatical information provided has a directly bearing on the result of grammatical information acquisition. We show that when provided with a knowledge rich lexical grammar, both the quantity and quality of the extracted knowledge improves substantially over the results with simple PS rules. 1 Background: Word Sketch Engine and Automatic Acquisition of Grammatical Information The original goal of corpus-based studies was to provide ‘a body of evidence’ for more theoretical linguistic studies (Francis and Kucera 1965). However, corpus-based studies evolved with the improvements made in electronic data manipulation, making of automatic acquisition of grammatical information a goal of computational linguistics, computational lexicography, as well as theoretical corpus linguistics. Previous works that made significant contribution to the study of automatic extraction of grammatical relation includes Sinclair’s (1987) work on KWIC, Church and Hanks’ (1989) introduction of Mutual Information, and Lin’s (1998) introduction of relevance measurement. Kilgarriff and colleagues’ work on Word Sktech Engine (WSE) makes a bold step forwards in automatic linguistic knowledge acquisition (Kilgarriff and Tudgell 2002, Kilgarriff et al. 2004). The main claim is that a ‘gargantuan’ corpus 1 contains enough distributional information about most grammatical dependencies in a language such that the set of simple collocational patterns will allow automatic extraction of grammatical relations and other grammatical information. Crucially, the validity of the extracted information does not rely on the preciseness of the rules or the perfect grammaticality of the data. Instead, WSE allows the presence of ungrammatical examples in the corpus and the possibility for collocational patterns to occasionally identify the wrong lexical pairs. WSE assumes that these anomalies will be statistically insignificant, especially when there are enough examples instantiating the intended grammatical information. In addition, WSE relies on Salience measurement to rank the significance of all attested relations. Salience is calculated by MI of a relation multiplied with the frequency of the relation, in order to correct MI’s bias towards low frequency items. WSE follows Lin’s (1998) formulation of MI of relations, where ||w1, R, w2|| stands for the frequency of the relation R between w1 and, w2. A wild card * can occurs in place of w1, R, or w2 to represent …

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