6,809 research outputs found
Learning Dictionaries for Named Entity Recognition using Minimal Supervision
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.Comment: In 14th Conference of the European Chapter of the Association for
Computational Linguistic, 201
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