Recent work has shown success in using continuous word embeddings learned from unlabeled data as features to improve supervised NLP systems, which is regarded as a simple semi-supervised learning mechanism. However, fundamental problems on effectively incorporating the word embedding features within the framework of linear models remain. In this study, we investigate and analyze three different approaches, including a new proposed distributional prototype approach, for utilizing the embedding features. The presented approaches can be integrated into most of the classical linear models in NLP. Experiments on the task of named entity recognition show that each of the proposed approaches can better utilize the word embedding features, among which the distributional prototype approach performs the best. Moreover, the combination of the approaches provides additive improvements, outperforming the dense and continuous embedding features by nearly 2 points of F1 score.
Revisiting Embedding Features for Simple Semi-supervised Learning
Jiang Guo,Wanxiang Che,Haifeng Wang,Ting Liu
Published 2014 in Conference on Empirical Methods in Natural Language Processing
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- Publication year
2014
- Venue
Conference on Empirical Methods in Natural Language Processing
- Publication date
2014-10-01
- Fields of study
Computer Science
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