Learning the space of word meanings for information retrieval systems

Several methods to represent meanings of words have been proposed. However. they are not useful for information retrieval systems because they cannot deal with the entities which cannot be universally represented by symbols.In this paper, we propose a notion of semantic space. Semantic space is an Euclidean space where words and entities are put. A word is one point in the space. The meanings of the word are represented as the space configuration around the word. The entities that cannot be represented by symbols can be identified in the space by the location the entity should be settled in. We also give a learning mechanism for the space. We prove the effectiveness of the proposed method by an experiment on information retrieval for the study of Japanese literature.