Paper: Learning Dictionaries for Named Entity Recognition using Minimal Supervision

ACL ID E14-1048
Title Learning Dictionaries for Named Entity Recognition using Minimal Supervision
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper describes an approach for au- tomatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Cor- relation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phrases using a small number of labeled examples. Our method achieves 16.5% and 11.3% F-1 score improvement over co-training on disease and virus NER re- spectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better perfor- mance compared to using word embed- dings.