Hypernym Discovery Based on Distributional Similarity and Hierarchical Structures

This paper presents a new method of developing a large-scale hyponymy relation database by combining Wikipedia and other Web documents. We attach new words to the hyponymy database extracted from Wikipedia by using distributional similarity calculated from documents on the Web. For a given target word, our algorithm first finds k similar words from the Wikipedia database. Then, the hypernyms of these k similar words are assigned scores by considering the distributional similarities and hierarchical distances in the Wikipedia database. Finally, new hyponymy relations are output according to the scores. In this paper, we tested two distributional similarities. One is based on raw verbnoun dependencies (which we call "RVD"), and the other is based on a large-scale clustering of verb-noun dependencies (called "CVD"). Our method achieved an attachment

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Ichiro Yamada, Kentaro Torisawa, Jun'ichi Kazama, Kow Kuroda, Masaki Murata, Stijn De Saeger, Francis Bond, Asuka Sumida
• Keywords: Database, Distributional Similarities, EMNLP 2009, Natural Language Processing, Wikipedia