Construction of an objective hierarchy of abstract concepts via directional similarity

Authors:
Kyoko Kanzaki;Eiko Yamamoto;Hitoshi Isahara;Qing Ma
Affiliations:
National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;National Institute of Information and Communications Technology, Kyoto, Japan;Ryukoku University, Otsu, Japan
Venue:
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Year:
2004

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Abstract

The method of organization of word meanings is a crucial issue with lexical databases. Our purpose in this research is to extract word hierarchies from corpora automatically. Our initial task to this end is to determine adjective hyperonyms. In order to find adjective hyperonyms, we utilize abstract nouns. We constructed linguistic data by extracting semantic relations between abstract nouns and adjectives from corpus data and classifying abstract nouns based on adjective similarity using a self-organizing semantic map, which is a neural network model (Kohonen 1995). In this paper we describe how to hierarchically organize abstract nouns (adjective hyperonyms) in a semantic map mainly using CSM. We compare three hierarchical organizations of abstract nouns, according to CSM, frequency (Tf.CSM) and an alternative similarity measure based on coefficient overlap, to estimate hyperonym relations between words.