Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
Van-Hien Tran, Van-Thuy Phi, Hiroyuki Shindo, Yuji Matsumoto
Abstract
Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.- Anthology ID:
- N19-1286
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2793–2798
- Language:
- URL:
- https://aclanthology.org/N19-1286
- DOI:
- 10.18653/v1/N19-1286
- Cite (ACL):
- Van-Hien Tran, Van-Thuy Phi, Hiroyuki Shindo, and Yuji Matsumoto. 2019. Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2793–2798, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (Tran et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1286.pdf