Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media

Gustavo Aguilar, Adrian Pastor López-Monroy, Fabio González, Thamar Solorio

Abstract
Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments.
Anthology ID:
N18-1127
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1401–1412
Language:
URL:
https://aclanthology.org/N18-1127
DOI:
10.18653/v1/N18-1127
Bibkey:
Cite (ACL):
Gustavo Aguilar, Adrian Pastor López-Monroy, Fabio González, and Thamar Solorio. 2018. Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1401–1412, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media (Aguilar et al., NAACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/N18-1127.pdf
Data
CoNLL 2003, IPM NEL, WNUT 2016 NER, WNUT 2017
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