Paper: Context Feature Selection for Distributional Similarity

ACL ID I08-1072
Title Context Feature Selection for Distributional Similarity
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2008

Distributional similarity is a widely used concept to capture the semantic relatedness ofwordsinvariousNLPtasks. 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...