SW-AG: Local Context Matching For English Substitution

Citation data:

SemEval '07 Proceedings Of The 4th International Workshop On Semantic Evaluations, Page: 304-307

Publication Year:
2007
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conference paper description
We present two systems that pick the ten most appropriate substitutes for a marked word in a test sentence. The first system scores candidates based on how frequently their local contexts match that of the marked word. The second system, an enhancement to the first, incorporates cosine similarity using unigram features. The core of both systems bypasses intermediate sense selection. Our results show that a knowledge-light, direct method for scoring potential replacements is viable.