Semi-supervised Word Sense Disambiguation with Neural Models
Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, Eric Altendorf
Abstract
Determining the intended sense of words in text – word sense disambiguation (WSD) – is a long-standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.- Anthology ID:
- C16-1130
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1374–1385
- Language:
- URL:
- https://aclanthology.org/C16-1130
- DOI:
- Cite (ACL):
- Dayu Yuan, Julian Richardson, Ryan Doherty, Colin Evans, and Eric Altendorf. 2016. Semi-supervised Word Sense Disambiguation with Neural Models. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1374–1385, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Semi-supervised Word Sense Disambiguation with Neural Models (Yuan et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1130.pdf
- Data
- Senseval-2