A Multi-task Approach for Named Entity Recognition in Social Media Data

Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López-Monroy, Thamar Solorio


Abstract
Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.
Anthology ID:
W17-4419
Volume:
Proceedings of the 3rd Workshop on Noisy User-generated Text
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–153
Language:
URL:
https://aclanthology.org/W17-4419
DOI:
10.18653/v1/W17-4419
Bibkey:
Cite (ACL):
Gustavo Aguilar, Suraj Maharjan, Adrian Pastor López-Monroy, and Thamar Solorio. 2017. A Multi-task Approach for Named Entity Recognition in Social Media Data. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 148–153, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A Multi-task Approach for Named Entity Recognition in Social Media Data (Aguilar et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4419.pdf
Code
 tavo91/NER-WNUT17
Data
WNUT 2017