Sciweavers

COLING
2008

Metric Learning for Synonym Acquisition

13 years 5 months ago
Metric Learning for Synonym Acquisition
The distance or similarity metric plays an important role in many natural language processing (NLP) tasks. Previous studies have demonstrated the effectiveness of a number of metrics such as the Jaccard coefficient, especially in synonym acquisition. While the existing metrics perform quite well, to further improve performance, we propose the use of a supervised machine learning algorithm that fine-tunes them. Given the known instances of similar 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.
Nobuyuki Shimizu, Masato Hagiwara, Yasuhiro Ogawa,
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Nobuyuki Shimizu, Masato Hagiwara, Yasuhiro Ogawa, Katsuhiko Toyama, Hiroshi Nakagawa
Comments (0)