Selective Sampling for Example-based Word Sense Disambiguation

This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense disambiguation (overhead for supervision) is required. In addition, the time complexity of searching a large-sized database poses a considerable problem (overhead for search). To counter these problems, our method selectively samples a smaller-sized effective subset from a given example set for use in word sense disambiguation. Our method is characterized by the reliance on the notion of training utility: the degree to which each example is informative for future example sampling when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectiveness of our method through experiments on about one thousand sentences. Compared to experiments with other example sampling methods, our method reduced both the overhead for supervision and the overhead for search, without the degeneration of the performance of the system.Comment: 25 pages, 14 Postscript figure

特定領域研究「日本語コーパス」平成20年度公開ワークショップ（研究成果報告会）予稿集

特定領域研究「日本語コーパス」平成20年度公開ワークショップ,東京工業大学大岡山キャンパスディジタル多目的ホール,2009年3月15-16日,特定領域研究「日本語コーパス」総括

Automatic Recognition of Verbal Polysemy

Polysemy is one of the major causes of difficulties in se- mantic clustering of words in a corpus. In this pal)er, we first; give a definition of polysemy from the viewpoint of clustering and then, based on this detinition, we prol)ose a clustering method which reeogniscs verbal 1)olysenfics fi'om a textual corpus. The results of experi,nensdemonstrate t;hc cffeetivencss of the prol) osed method