WiNER: A Wikipedia Annotated Corpus for Named Entity Recognition

Abbas Ghaddar, Phillippe Langlais


Abstract
We revisit the idea of mining Wikipedia in order to generate named-entity annotations. We propose a new methodology that we applied to English Wikipedia to build WiNER, a large, high quality, annotated corpus. We evaluate its usefulness on 6 NER tasks, comparing 4 popular state-of-the art approaches. We show that LSTM-CRF is the approach that benefits the most from our corpus. We report impressive gains with this model when using a small portion of WiNER on top of the CONLL training material. Last, we propose a simple but efficient method for exploiting the full range of WiNER, leading to further improvements.
Anthology ID:
I17-1042
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
413–422
Language:
URL:
https://www.aclweb.org/anthology/I17-1042
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-1042.pdf