Abstract
We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.- Anthology ID:
- P18-1144
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1554–1564
- Language:
- URL:
- https://aclanthology.org/P18-1144
- DOI:
- 10.18653/v1/P18-1144
- Cite (ACL):
- Yue Zhang and Jie Yang. 2018. Chinese NER Using Lattice LSTM. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1554–1564, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Chinese NER Using Lattice LSTM (Zhang & Yang, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/P18-1144.pdf
- Code
- jiesutd/LatticeLSTM + additional community code
- Data
- Resume NER, MSRA CN NER, OntoNotes 4.0, Weibo NER