Combining knowledge and corpus-based measures for word-to-word similarity
Abstract
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
Recommended Citation
Ştefənescu, D., Rus, V., Niraula, N., & Banjade, R. (2014). Combining knowledge and corpus-based measures for word-to-word similarity. Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014, 87-90. Retrieved from https://digitalcommons.memphis.edu/facpubs/2618
