Paper: Metric Learning for Synonym Acquisition

ACL ID C08-1100
Title Metric Learning for Synonym Acquisition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008

The distance or similarity metric plays an important role in many natural language processing (NLP) tasks. Previous stud- ies have demonstrated the effectiveness of a number of metrics such as the Jaccard coefficient, especially in synonym acqui- sition. While the existing metrics per- form quite well, to further improve perfor- mance, we propose the use of a supervised machine learning algorithm that fine-tunes them. Given the known instances of sim- ilar or dissimilar words, we estimated the parameters of the Mahalanobis distance. We compared a number of metrics in our experiments, and the results show that the proposed metric has a higher mean average precision than other metrics.