Paper Info
Title
Sparsity and normalization in word similarity systems
Abstract
We investigate the problem of improving performance in distributional word similarity systems trained on sparse data, focusing on a family of similarity functions we call Dice-family functions (Dice 1945 Ecology 26(3): 297-302), including the similarity function introduced in Lin (1998 Proceedings of the 15th International Conference on Machine Learning, 296-304), and Curran (2004 PhD thesis, University of Edinburgh. College of Science and Engineering. School of Informatics), as well as a generalized version of Dice Coefficient used in data mining applications (Strehl 2000, 55). We propose a generalization of the Dice-family functions which uses a weight parameter a to make the similarity functions asymmetric. We show that this generalized family of functions (a systems) all belong to the class of asymmetric models first proposed in Tversky (1977 Psychological Review 84: 327-352), and in a multi-task evaluation of ten word similarity systems, we show that a systems have the best performance across word ranks. In particular, we show that a-parameterization substantially improves the correlations of all Dice-family functions with human judgements on three words sets, including the Miller-Charles/Rubenstein Goodenough word set (Miller and Charles 1991 Language and Cognitive Processes 6(1):1-28; Rubenstein and Goodenough 1965 Communications of the ACM 8:627-633).
Year
DOI
Venue
2016
10.1017/S1351324915000261
NATURAL LANGUAGE ENGINEERING
Field
DocType
Volume
Normalization (statistics),Computer science,Speech recognition,Natural language processing,Artificial intelligence
Journal
22
Issue
ISSN
Citations 
3.0
1351-3249
0
PageRank 
References 
Authors
0.34
19
2
Name
Order
Citations
PageRank
Jean Mark Gawron118751.92
Kellen Stephens200.68