The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Modeling Joint Entity and Relation Extraction with Table Representation
Open accessAbstract
This paper proposes a history-based structured learning approach that jointly extracts entities and relations in a sentence.We introduce a novel simple and flexible table representation of entities and relations.We investigate several feature settings, search orders, and learning methods with inexact search on the table.The experimental results demonstrate that a joint learning approach significantly outperforms a pipeline approach by incorporating global features and by selecting appropriate learning methods and search orders.
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A survey of named entity recognition and classification
Nadeau, D., & Sekine, S. (2007) · Lingvisticae Investigationes · doi:10.1075/li.30.1.03nad · Cited by 2,507
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Design challenges and misconceptions in named entity recognition
Ratinov, L., & Roth, D. (2009) · Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09 · doi:10.3115/1596374.1596399 · Cited by 1,448
Large margin classification using the perceptron algorithm
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Computer Intensive Methods for Testing Hypotheses: An Introduction
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Kernel methods for relation extraction
Zelenko, D., Aone, C., & Richardella, A. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118703 · Cited by 573
Incremental Joint Extraction of Entity Mentions and Relations
Li, Q., & Ji, H. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1038 · Cited by 535
Chunking with support vector machines
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Cited by
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1113 · Cited by 787
A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531
Entity-Relation Extraction as Multi-Turn Question Answering
Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1129 · Cited by 336
REBEL: Relation Extraction By End-to-end Language generation
Huguet Cabot, P.-L., & Navigli, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.204 · Cited by 261
Metadata
- Authors
- Miwa, M., & Sasaki, Y.
- Venue
- Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2014
- Pages
- 1858-1869
- OpenAlex
- W2251091211
- Cited by
- 369
- References
- 27
- FWCI
- 3.36
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Miwa, M., & Sasaki, Y. (2014). Modeling Joint Entity and Relation Extraction with Table Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/d14-1200
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
