Abstract
We propose a neural network model for coordination boundary detection. Our method relies on the two common properties - similarity and replaceability in conjuncts - in order to detect both similar pairs of conjuncts and dissimilar pairs of conjuncts. The model improves identification of clause-level coordination using bidirectional RNNs incorporating two properties as features. We show that our model outperforms the existing state-of-the-art methods on the coordination annotated Penn Treebank and Genia corpus without any syntactic information from parsers.- Anthology ID:
- I17-1027
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 264–272
- Language:
- URL:
- https://aclanthology.org/I17-1027
- DOI:
- Cite (ACL):
- Hiroki Teranishi, Hiroyuki Shindo, and Yuji Matsumoto. 2017. Coordination Boundary Identification with Similarity and Replaceability. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 264–272, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Coordination Boundary Identification with Similarity and Replaceability (Teranishi et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/I17-1027.pdf