Context Feature Selection for Distributional Similarity
Masato Hagiwara, Yasuhiro Ogawa, Katsuhiko Toyama · 2008
Distributional similarity is a widely used concept to capture the semantic relatedness of words in various NLP tasks. However, ac-curate similarity calculation requires a large number of contexts, which leads to imprac-tically high computational complexity. To alleviate the problem, we have investigated the effectiveness of automatic context selec-tion by applying feature selection methods explored mainly for text categorization. Our experiments on synonym acquisition have shown that while keeping or sometimes in-creasing the performance, we can drastically reduce the unique contexts up to 10 % of the original size. We have also extended the measures so that they cover context cate-gories. The result shows a considerable cor-relation between the measures and the per-formance, enabling the automatic selection of effective context categories for distribu-tional similarity. 1