Request Correction

Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense

Multi-sense word embedding is an important extension of neural word embeddings. By leveraging context of each word instance, multi-prototype version of word embeddings were accomplished to represent the multi-senses. Unfortunately, this kind of context based approach inevitably produces multiple senses which should actually be a single one, suffering from the various context of a word. Shi et al.(2016) used WordNet to evaluate the neighborhood similarity of each sense pair to detect such pseudo multi-senses. In this paper, a novel framework for unsupervised corpus sense tagging is presented, which mainly contains four steps: (a) train multi-sense word embeddings on the given corpus, using existing multi-sense word embedding frameworks; (b) detect pseudo multi-senses in the obtained embeddings, without requirement to any extra language resources; (c) label each word in the corpus with a specific sense tag, with respect to the result of pseudo multi-sense detection; (d) re-train multi-sense word embeddings with the pre-selected sense tags. We evaluate our framework by training word embeddings with the obtained sense specific corpus. On the tasks of word similarity, word analogy as well as sentence understanding, the embeddings trained on sense-specific corpus obtain better results than the basic strategy which is applied in step (a).