Dynamic Sense Representation Using Conceptual Fuzzy Sets

Abstract

The ambiguity in language is one of the most difficult problems in dealing with word senses using computers. Word senses vary dynamically depending on context. We need to specify the context to identify these. However, context also varies depending on granularity and the viewpoint of the topic. Therefore, generally speaking, people pay attention to the part of the attributes of the entity, which the dictionary definition of the word indicates, depending on such variant contexts. We call this "aspectual sense." In this paper, we propose a method to represent such senses using conceptual fuzzy sets. First we generate atomic conceptual fuzzy sets automatically using word sequences just before the target word and the modified confabulation model (a prediction method similar to the n-gram model). Then we assign a word to the appropriate fuzzy set using a method based on co-occurrences. Based on an experiment using a large corpus, which was the AQUAINT collection consisting of 1 million newswire text data in English compiled from three sources, we generated each atomic conceptual fuzzy set expressed in the aspectual sense depending on variant contexts. Then we experimented using a few keywords, phrased like short queries, in a general information retrieval task, which is a difficult situation to extract context. The results of this task demonstrated that each assigned fuzzy set corresponding to context predicted by the few keywords was appropriate.