Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence
Source abstract
Abstract
Relation extraction is a type of information extraction task that recognizes semantic relationships between entities in a sentence. Many previous studies have focused on extracting only one semantic relation between two entities in a single sentence. However, multiple entities in a sentence are associated through various relations. To address this issue, we proposed a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject–object relations using a forward object decoder. Then, it finds 1-to-n subject–object relations using a backward subject decoder. Our experiments confirmed that the proposed model outperformed previous models, with an F1-score of 80.8% for the ACE (automatic content extraction) 2005 corpus and an F1-score of 78.3% for the NYT (New York Times) corpus.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Sentiment Analysis and Opinion Mining · Biomedical Text Mining and Ontologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer and Communications Engineering, Kangwon National University, Chuncheon 24341, Korea
Computer Science and Engineering, Konkuk University, Seoul 05029, Korea
Funding
Ministry of Science and ICT, South Korea
References (25)
Show source-provided references
1. Extraction of Instances with Social Relations for Automatic Construction of a Social Network
2. Zeng, X., Zeng, D., He, S., Liu, K., and Zhao, J. (2018, January 15–20). Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia.10.18653/v1/P18-1047 ↗
3. Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. (2014, January 23–29). Relation Classification via Convolutional Deep Neural Network. Proceedings of the 24th International Conference on Computational Linguistics, Dublin, Ireland.
4. Nguyen, T.H., and Grishman, R. (June, January 31). Relation extraction: Perspective from convolutional neural networks. Proceedings of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Denver, CO, USA.
5. Yu, J., and Jiang, J. (2016, January 11–16). Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network. Proceedings of the 26th International Conference on Computational Linguistics, Osaka, Japan.
6. Zhang, Y., Qi, P., and Manning, C.D. (November, January 31). Graph Convolution over Pruned Dependency Trees Improves Relation Extraction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.10.18653/v1/D18-1244 ↗
7. Miwa, M., and Bansal, N. (2016, January 7–12). End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.10.18653/v1/P16-1105 ↗
8. Zhang, Y., Zhong, V., Chen, D., Angeli, G., and Manning, C.D. (2017, January 9–11). Positionaware Attention and Supervised Data Improve Slot Filling. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.10.18653/v1/D17-1004 ↗
9. Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., and Hajishirzi, H. (2019, January 2–7). A General Framework for Information Extraction using Dynamic Span Graphs. Proceedings of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, Minneapolis, MN, USA.10.18653/v1/N19-1308 ↗
10. Vinyals, O., Fortunato, M., and Jaitly, N. (2015, January 7–12). Pointer Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
11. Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7–9). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the International Conference on Learning Representations 2015 (ICLR 2015), San Diego, CA, USA.
12. Pennington, J., Socher, R., and Manning, C.D. (2014, January 25–29). GloVe: Global Vectors for Word Representation. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.10.3115/v1/D14-1162 ↗
13. Named Entity Recognizer Using Gloval Vector and Convolutional Neural Network Embedding
14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention All You Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
15. (2020, May 31). ACE 2005 Multilingual Training Corpus. Available online: https://catalog.ldc.upenn.edu/LDC2006T06.
16. Ren, X., Wu, Z., He, W., Qu, M., Voss, C.R., Ji, H., Abdelzaher, T.F., and Han, J. (2017, January 3–7). Cotype: Joint Extraction of Typed Entities and Relations with Knowledge Bases. Proceedings of the International World Wide Web Conference, Perth, Australia.10.1145/3038912.3052708 ↗
17. Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., and Xu, B. (August, January 30). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada.
18. Seo, M., Kembhavi, A., Farhadi, A., and Hajishirz, H. (2017, January 24–26). Bi-Directional Attention Flow for Machine Comprehension. Proceedings of the International Conference on Learning Representations 2017 (ICLR 2017), Toulon, France.
19. Gormley, M.R., Yu, M., and Dredze, M. (2015, January 17–21). Improved Relation Extraction with Feature-Rich Compositional Embedding Models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.10.18653/v1/D15-1205 ↗
20. Dixit, K., and Onaizan, Y.A. (August, January 28). Span-Level Model for Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
21. Kim, S., and Choi, S. (2018, January 20–22). Relation Extraction using Hybrid Convolutional and Recurrent Networks. Proceedings of the Korea Computer Congress 2018 (KCC 2018), Jeju, Korea. (In Korean).
22. Christopoulou, F., Miwa, M., and Ananiadou1, S. (2018, January 15–20). A Walk-based Model on Entity Graphs for Relation Extraction. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia.10.18653/v1/P18-2014 ↗
23. Fu, T.J., Li, P.H., and Ma, W.Y. (August, January 28). GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy.
24. Kipf, T., and Welling, M. (2017, January 24–26). Semisupervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations 2017 (ICLR 2017), Toulon, France.
25. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2–7). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the North American Chapter of the Association for Computational Linguistics on Human Language Technologies, Minneapolis, MN, USA.