A Unified Model for Word Sense Representation and Disambiguation
Xinxiong Chen, Zhiyuan Liu, Maosong Sun · 2014
Most word representation methods assume that each word owns a single semantic vec-tor. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, which will assign distinct representation-s for each word sense.1 The basic idea is that both word sense representation (WS-R) and word sense disambiguation (WS-D) will benefit from each other: (1) high-quality WSR will capture rich informa-tion about words and senses, which should be helpful for WSD, and (2) high-quality WSD will provide reliable disambiguat-ed corpora for learning better sense rep-resentations. Experimental results show that, our model improves the performance of contextual word similarity compared to existing WSR methods, outperforms state-of-the-art supervised methods on domain-specific WSD, and achieves competitive performance on coarse-grained all-words WSD. 1