Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media

Patrick Jansson, Shuhua Liu


Abstract
This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.
Anthology ID:
W17-4420
Volume:
Proceedings of the 3rd Workshop on Noisy User-generated Text
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Leon Derczynski, Wei Xu, Alan Ritter, Tim Baldwin
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
154–159
Language:
URL:
https://aclanthology.org/W17-4420
DOI:
10.18653/v1/W17-4420
Bibkey:
Cite (ACL):
Patrick Jansson and Shuhua Liu. 2017. Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 154–159, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media (Jansson & Liu, WNUT 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-4420.pdf
Data
IPM NELWNUT 2017