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Improving Twitter Named Entity Recognition using Word Representations
2015
Proceedings of the Workshop on Noisy User-generated Text
This paper describes our system used in the ACL 2015 Workshop on Noisy Usergenerated Text Shared Task for Named Entity Recognition (NER) in Twitter. Our system uses Conditional Random Fields to train two separate classifiers for the two evaluations: predicting 10 fine-grained types, and segmenting named entities. We focus our efforts on generating word representations from large amount of unlabeled newswire data and tweets. Our experiment results show that cluster features derived from word
doi:10.18653/v1/w15-4321
dblp:conf/aclnut/TohCS15
fatcat:di3hsjjlkfd7jbyigoihn6lajq