Word Sense Disambiguation
Based on Word Similarity Calculation Using Word Vector Representation from a
Knowledge-based Graph
Word sense disambiguation (WSD) is the task to determine
the sense of an ambiguous word according to its context. Many existing WSD
studies have been using an external knowledge-based unsupervised approach
because it has fewer word set constraints than supervised approaches requiring
training data. In this paper, we propose a new WSD method to generate the context
of an ambiguous word by using similarities between an ambiguous word and words
in the input document. In addition, to leverage our WSD method, we further
propose a new word similarity calculation method based on the semantic network
structure of BabelNet. We evaluate the proposed methods on the SemEval-2013 and
SemEval-2015 for English WSD dataset. Experimental results demonstrate that the
proposed WSD method significantly improves the baseline WSD method.
Furthermore, our WSD system outperforms the state-of-the-art WSD systems in the
Semeval-13 dataset. Finally, it has higher performance than the state-of-the-art
unsupervised knowledge-based WSD system in the average performance of both
datasets.
COLING 2018 will be held in Santa Fe, New-Mexico, USA, August 20-26, 2018.
