DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets

Fabrice Dugas, Eric Nichols


Abstract
In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task #2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and Nichols (2016), as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large-scale Web text collections together with text normalization to cope with the diversity in Web texts, and lexicons for target named entity classes constructed from publicly-available sources. Extended evaluation comparing the effectiveness of various word embeddings, text normalization, and lexicon settings shows that our system achieves a maximum F1-score of 47.24, performance surpassing that of the shared task’s second-ranked system.
Anthology ID:
W16-3924
Volume:
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Bo Han, Alan Ritter, Leon Derczynski, Wei Xu, Tim Baldwin
Venue:
WNUT
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
178–187
Language:
URL:
https://aclanthology.org/W16-3924
DOI:
Bibkey:
Cite (ACL):
Fabrice Dugas and Eric Nichols. 2016. DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 178–187, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets (Dugas & Nichols, WNUT 2016)
Copy Citation:
PDF:
https://aclanthology.org/W16-3924.pdf
Data
DBpediaOntoNotes 5.0WNUT 2016 NER