Combining knowledge and corpus-based measures for word-to-word similarity


This paper shows that the combination of knowledge and corpus-based word-to-word similarity measures can produce higher agreement with human judgment than any of the individual measures. While this might be a predictable result, the paper provides insights about the circumstances under which a combination is productive and about the improvement levels that are to be expected. The experiments presented here were conducted using the word-to-word similarity measures included in SEMILAR, a freely available semantic similarity toolkit.

Publication Title

Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014

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