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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/D17-1282.pdf
Attachment:
 D17-1282.Attachment.zip
Code
 jacobvsdanniel/tf_rnn
Data
CoNLL 2003OntoNotes 5.0