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Automatic Acquisition of Qualia Structure from Corpus Data Ichiro YAMADA Timothy BALDWIN Hideki SUMIYOSHI Masahiro SHIBATA Nobuyuki YAGI Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.10 pp.1534-1541 Publication Date: 2007/10/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.10.1534 Print ISSN: 0916-8532 Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System) Category: Keyword: qualia structure, maximum entropy learning, ranking of word relevance, spearman's rank correlation, lexical knowledge acquisition, Full Text: PDF(375.6KB)>>
Summary: This paper presents a method to automatically acquire a given noun's telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which makes use of template-based contextual features derived from token instances of each noun. The output of our method is a ranked list of verbs for each noun, across the different qualia roles. We also propose a variant of Spearman's rank correlation to evaluate the correlation of two top-N ranked lists. Using this correlation method, we represent the ability of the proposed method to identify qualia structure relative to a conventional template-based method. | ||||||||||
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