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:
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/C16-1130.pdf
Data
Senseval-2