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
- Editors:
- Bo Han, Alan Ritter, Leon Derczynski, Wei Xu, Tim Baldwin
- Venue:
- WNUT
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 153–163
- Language:
- URL:
- https://aclanthology.org/W16-3921
- DOI:
- Cite (ACL):
- Kurt Junshean Espinosa, Riza Theresa Batista-Navarro, and Sophia Ananiadou. 2016. Learning to recognise named entities in tweets by exploiting weakly labelled data. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 153–163, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Learning to recognise named entities in tweets by exploiting weakly labelled data (Espinosa et al., WNUT 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-3921.pdf
- Data
- WNUT 2016 NER