Learning to recognise named entities in tweets by exploiting weakly labelled data

Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, Sophia Ananiadou


Abstract
Named entity recognition (NER) in social media (e.g., Twitter) is a challenging task due to the noisy nature of text. As part of our participation in the W-NUT 2016 Named Entity Recognition Shared Task, we proposed an unsupervised learning approach using deep neural networks and leverage a knowledge base (i.e., DBpedia) to bootstrap sparse entity types with weakly labelled data. To further boost the performance, we employed a more sophisticated tagging scheme and applied dropout as a regularisation technique in order to reduce overfitting. Even without hand-crafting linguistic features nor leveraging any of the W-NUT-provided gazetteers, we obtained robust performance with our approach, which ranked third amongst all shared task participants according to the official evaluation on a gold standard named entity-annotated corpus of 3,856 tweets.
Anthology ID:
W16-3921
Volume:
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
WNUT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
153–163
Language:
URL:
https://www.aclweb.org/anthology/W16-3921
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-3921.pdf