Acquiring Hyponymy Relations from Web Documents

Acquiring Hyponymy Relations from Web Documents Keiji Shinzato Kentaro Torisawa School of Information Science, Japan Advanced Institute of Science and Technology (JAIST) 1-1 Asahidai, Tatsunokuchi, Nomi-gun, Ishikawa, 923-1292 JAPAN {skeiji,torisawa}@jaist.ac.jp Abstract This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural lan- guage processing systems. Most existing ac- quisition methods for hyponymy relations rely on particular linguistic patterns, such as “NP such as NP”. Our method, however, does not use such linguistic patterns, and we expect that our procedure can be applied to a wide range of expressions for which existing meth- ods cannot be used. Our acquisition algo- rithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences. 1 Introduction The goal of this work is to become able to automatically acquire hyponymy relations for a wide range of words or phrases from HTML documents on the WWW. We do not use particular lexicosyntactic patterns, as previous at- tempts have (Hearst, 1992; Caraballo, 1999; Imasumi, 2001; Fleischman et al., 2003; Morin and Jacquemin, 2003; Ando et al., 2003). The frequencies of use for such lexicosyntactic patterns are relatively low, and there can be many words or phrases that do not appear in such pat- terns even if we look at a large number of texts. The effort of searching for other clues indicating hyponymy rela- tions is thus significant. We try to acquire hyponymy re- lations by combining three different types of clue obtain- able from a wide range of words or phrases. The first type of clue is inclusion in itemizations or lists found in typi- cal HTML documents on the WWW. The second consists of statistical measures such as the document frequency (df) and the inverse document frequency (idf), which are ・ Car Specification ・ Toyota ・ Honda ・ Nissan Figure 1: An example of itemization popular in the IR literature. The third is verb-noun co- occurrence in normal corpora. In our acquisition, we made the following assumptions. Assumption A Expressions included in the same item- ization or listing in an HTML document are likely to have a common hypernym. Assumption B Given a set of hyponyms that have a common hypernym, the hypernym appears in many documents that include the hyponyms. Assumption C Hyponyms and their hypernyms are se- mantically similar. Our acquisition process computes a common hyper- nym for expressions in the same itemizations. It pro- ceeds as follows. First, we download a large number of HTML documents from the WWW and extract a set of natural language expressions that are listed in the same itemized region of documents. Consider the item- ization in Fig. 1. We extract the set of expressions, {Toyota, Honda, Nissan} from it. From Assumption A, we can treat these expressions as candidates of hyponyms that have a common hypernym such as “company”. We call such expressions in the same itemization hyponym candidates. Particularly, …

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