This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation 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 respectively. We also show that by adding candidate phrase embeddings as features in a sequence tagger gives better performance compared to using word embeddings. © 2014 Association for Computational Linguistics.
CITATION STYLE
Neelakantan, A., & Collins, M. (2014). Learning dictionaries for named entity recognition using minimal supervision. In 14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014 (pp. 452–461). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/e14-1048
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