Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-Chieh Chou, Wei-Yun Ma
Abstract
In this paper, we utilize the linguistic structures of texts to improve named entity recognition by BRNN-CNN, a special bidirectional recursive network attached with a convolutional network. Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition. In contrast to classical sequential labeling methods, the system first identifies which text chunks are possible named entities by whether they are linguistic constituents. Then it classifies these chunks with a constituency tree structure by recursively propagating syntactic and semantic information to each constituent node. This method surpasses current state-of-the-art on OntoNotes 5.0 with automatically generated parses.- Anthology ID:
- D17-1282
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2664–2669
- Language:
- URL:
- https://aclanthology.org/D17-1282
- DOI:
- 10.18653/v1/D17-1282
- Cite (ACL):
- Peng-Hsuan Li, Ruo-Ping Dong, Yu-Siang Wang, Ju-Chieh Chou, and Wei-Yun Ma. 2017. Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2664–2669, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks (Li et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D17-1282.pdf
- Code
- jacobvsdanniel/tf_rnn
- Data
- CoNLL 2003, OntoNotes 5.0