Clement T. Yu
Also published as: Clement Yu
A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment
Jing Wang | Mohit Bansal | Kevin Gimpel | Brian D. Ziebart | Clement T. Yu
Transactions of the Association for Computational Linguistics, Volume 3
Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.
Polarity Consistency Checking for Sentiment Dictionaries
Eduard Dragut | Hong Wang | Clement Yu | Prasad Sistla | Weiyi Meng
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Jing Wang 1
- Mohit Bansal 1
- Kevin Gimpel 1
- Brian D. Ziebart 1
- Eduard Dragut 1
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