Paper: Compound Embedding Features for Semi-supervised Learning

ACL ID N13-1063
Title Compound Embedding Features for Semi-supervised Learning
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Compound Embedding Features for Semi-supervised Learning Mo Yu1, Tiejun Zhao1, Daxiang Dong2, Hao Tian2 and Dianhai Yu2 Harbin Institute of Technology, Harbin, China Baidu Inc., Beijing, China {yumo,tjzhao}@mtlab.hit.edu.cn {dongdaxiang,tianhao,yudianhai}@baidu.com Abstract To solve data sparsity problem, recently there has been a trend in discriminative methods of NLP to use representations of lexical items learned from unlabeled data as features. In this paper, we investigated the usage of word representations learned by neural language models, i.e. word embeddings. The direct us-age has disadvantages such as large amount of computation, inadequacy with dealing word ambiguity and rare-words, and the problem of linear non-separability. To overcome these problems, we instead built ...