Semantic Word Clusters Using Signed Spectral Clustering

João Sedoc, Jean Gallier, Dean Foster, Lyle Ungar


Abstract
Vector space representations of words capture many aspects of word similarity, but such methods tend to produce vector spaces in which antonyms (as well as synonyms) are close to each other. For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights. We present a new signed spectral normalized graph cut algorithm, signed clustering, that overlays existing thesauri upon distributionally derived vector representations of words, so that antonym relationships between word pairs are represented by negative weights. Our signed clustering algorithm produces clusters of words that simultaneously capture distributional and synonym relations. By using randomized spectral decomposition (Halko et al., 2011) and sparse matrices, our method is both fast and scalable. We validate our clusters using datasets containing human judgments of word pair similarities and show the benefit of using our word clusters for sentiment prediction.
Anthology ID:
P17-1087
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
939–949
Language:
URL:
https://www.aclweb.org/anthology/P17-1087
DOI:
10.18653/v1/P17-1087
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1087.pdf
Note:
 P17-1087.Notes.pdf
Video:
 https://vimeo.com/234958514