Segment-Level Neural Conditional Random Fields for Named Entity Recognition

Motoki Sato, Hiroyuki Shindo, Ikuya Yamada, Yuji Matsumoto


Abstract
We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.
Anthology ID:
I17-2017
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
97–102
Language:
URL:
https://aclanthology.org/I17-2017
DOI:
Bibkey:
Cite (ACL):
Motoki Sato, Hiroyuki Shindo, Ikuya Yamada, and Yuji Matsumoto. 2017. Segment-Level Neural Conditional Random Fields for Named Entity Recognition. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 97–102, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Segment-Level Neural Conditional Random Fields for Named Entity Recognition (Sato et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2017.pdf
Data
CoNLL-2003