Abstract
In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task #2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and Nichols (2016), as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large-scale Web text collections together with text normalization to cope with the diversity in Web texts, and lexicons for target named entity classes constructed from publicly-available sources. Extended evaluation comparing the effectiveness of various word embeddings, text normalization, and lexicon settings shows that our system achieves a maximum F1-score of 47.24, performance surpassing that of the shared task’s second-ranked system.- Anthology ID:
- W16-3924
- 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:
- 178–187
- Language:
- URL:
- https://aclanthology.org/W16-3924
- DOI:
- Cite (ACL):
- Fabrice Dugas and Eric Nichols. 2016. DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 178–187, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets (Dugas & Nichols, WNUT 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-3924.pdf
- Data
- DBpedia, WNUT 2016 NER