The semantic similarity (or distance) between words is one of the basic knowledge in Natural Language Processing. There have been several previous studies on measuring the similarity (or distance) based on word vectors in a multi-dimensional space. In those studies, high dimensional feature vectors of words are made from words' cooccurrence in a corpus or from reference relation in a dictionary, and then the word vectors are calculated from the feature vectors through the method like principal component analysis. This paper proposes a new placement method of nouns into a multi-dimensional space based on words' cooccurrence in a corpus. The proposed method doesn't use the high dimensional feature vectors of words, but is based on the idea that "vectors corresponding to nouns which cooccur with a word w in a relation f constitute a group in the multi-dimensional space". Although the whole meaning of nouns isn't reflected in the word vectors obtained by the pro posed method, the semantic similarity (or distance) between nouns defined with the word vectors is proper for an example-based disambiguation method. Copyright (c) 2004 JSAI.
|Number of pages||9|
|Journal||Transactions of the Japanese Society for Artificial Intelligence|
|Publication status||Published - Dec 1 2004|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence