[Objective] This paper proposes a model for entity coreference resolution, which integrates neural network and global reasoning. It tries to address the issues of complex entity information in the text as well as the ambiguity and sparse distribution of referential information. [Methods] First, we used the neural network model to extract the entities and their antecedents from the documents. Then, we combined the context information of the sentence to perform global reasoning. Finally, we added the reasoning results to the neural network model to improve the accuracy of entity coreference resolution. [Results] We examined our new model on the OntoNotes 5.0 dataset. The new model’s F1 score reached 74.76% under the CoNLL evaluation standard. [Limitations] More precise knowledge reasoning needs to be added. [Conclusions] Compared with the existing models, the proposed algorithm improves the coreference resolution performance and better understand text semantic information.
周宁, 靳高雅, 石雯茜. 融合神经网络与全局推理的实体共指消解算法*[J]. 数据分析与知识发现, 2022, 6(8): 75-83.
Zhou Ning, Jin Gaoya, Shi Wenqian. Algorithm for Entity Coreference Resolution with Neural Network and Global Reasoning. Data Analysis and Knowledge Discovery, 2022, 6(8): 75-83.
Brennan S E, Friedman M W, Pollard C J. A Centering Approach to Pronouns[C]// Proceedings of the 25th Annual Meeting on Association for Computational Linguistics. 1987: 155-162.
[3]
Lappin S, Leass H J. An Algorithm for Pronominal Anaphora Resolution[J]. Computational Linguistics, 1994, 20(4): 535-561.
[4]
Lee H, Chang A, Peirsman Y, et al. Deterministic Coreference Resolution Based on Entity-Centric, Precision-Ranked Rules[J]. Computational Linguistics, 2013, 39(4): 885-916.
doi: 10.1162/COLI_a_00152
[5]
Grosz B J, Weinstein S, Joshi A K. Centering: A Framework for Modeling the Local Coherence of Discourse[J]. Computational Linguistics, 1995, 21(2): 203-225.
[6]
Soon W M, Ng H T, Lim D C Y. A Machine Learning Approach to Coreference Resolution of Noun Phrases[J]. Computational Linguistics, 2001, 27(4): 521-544.
doi: 10.1162/089120101753342653
[7]
Ng V, Cardie C. Improving Machine Learning Approaches to Coreference Resolution[C]// Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. 2002: 104-111.
[8]
Lee H, Surdeanu M, Jurafsky D. A Scaffolding Approach to Coreference Resolution Integrating Statistical and Rule Based Models[J]. Natural Language Engineering, 2017, 23(5): 733-762.
doi: 10.1017/S1351324917000109
(Qian Wei, Guo Yikun, Zhou Yaqian, et al. English Noun Phrase Coreference Resolution via a Maximum Entropy Model[J]. Journal of Computer Research and Development, 2003, 40(9): 1337-1343.)
[10]
Mitkov R, Evans R, Orasan C. A New, Fully Automatic Version of Mitkov’s Knowledge Poor Pronoun Resolution Method[C]// Proceedings of the 3rd International Conference on Intelligent Text Processing and Computational Linguistics. 2002:168-186.
[11]
Ni Y G, Hale J, Eugene C. A Statistical Approach to Anaphora Resolution[C]// Proceedings of the 6th Workshop on Very Large Corpora. 1998: 161-170.
[12]
Wiseman S, Rush A M, Shieber S, et al. Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015: 1416-1426.
[13]
Lee K, He L H, Lewis M, et al. End-to-End Neural Coreference Resolution[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017:188-197.
[14]
Zhang R, dos Santos C N, Yasunaga M, et al. Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Volume 2:Short Papers). 2018: 102-107.
[15]
Wiseman S, Rush A M, Shieber S M. Learning Global Features for Coreference Resolution[OL]. arXivPreprint,arXiv:1604.03035.
(Teng Jiayue, Li Peifeng, Zhu Qiaoming, et al. Global Inference for Co-reference Resolution Between Chinese Events[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 52(1): 97-103.)
(Zhong Weifeng, Yang Hang, Chen Yubo, et al. Document-Level Event Extraction Based on Joint Labeling and Global Reasoning[J]. Journal of Chinese Information Processing, 2019, 33(9): 88-95.)
[18]
Clark K, Manning C D. Entity-Centric Coreference Resolution with Model Stacking[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1:Long Papers). 2015: 1405-1415.
[19]
Clark K, Manning C D. Deep Reinforcement Learning for Mention-Ranking Coreference Models[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2256-2262.
[20]
Clark K, Manning C D. Improving Coreference Resolution by Learning Entity Level Distributed Representations[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016: 643-653.
[21]
Zhang Z Y, Han X, Liu Z Y, et al. ERNIE: Enhanced Language Representation with Informative Entities[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 1441-1451.
[22]
Lee K, He L H, Zettlemoyer L. Higher-Order Coreference Resolution with Coarse-to-fine Inference[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 2 (Short Papers). 2018: 687-692.
[23]
Khosla S, Rose C. Using Type Information to Improve Entity Coreference Resolution[OL]. arXiv Preprint, arXiv:2010.05738.
[24]
Joshi M, Levy O, Zettlemoyer L, et al. BERT for Coreference Resolution: Baselines and Analysis[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. 2019: 5803-5808.
[25]
Joshi M, Chen D Q, Liu Y H, et al. SpanBERT: Improving Pre-training by Representing and Predicting Spans[J]. Transactions of the Association for Computational Linguistics, 2020, 8: 64-77.
doi: 10.1162/tacl_a_00300
[26]
Aralikatte R, Lamm M, Hardt D, et al. Ellipsis and Coreference Resolution as Question Answering[OL]. arXiv Preprint, arXiv:1908.11141.
[27]
Wu W, Wang F, Yuan A, et al. CorefQA: Coreference Resolution as Query-Based Span Prediction[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 6953-6963.
[28]
Dobrovolskii V. Word-Level Coreference Resolution[C]// Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021:7670-7675.
[29]
Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1532-1543.
[30]
Peters M E, Neumann M, Iyyer M, et al. Deep Contextualized Word Representations[OL]. arXiv Preprint, arXiv:1802.05365.
(Jin Chen, Li Weihua, Ji Chen, et al. Bi-directional Long Short-term Memory Neural Networks for Chinese Word Segmentation[J]. Journal of Chinese Information Processing, 2018, 32(2): 29-37.)
[33]
Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need[OL]. arXiv Preprint, arXiv:1706.03762.
[34]
Hirschman L, Robinson P, Burger J D, et al. Automating Coreference: The Roal of Annotated Training Data[C]// Proceedings of the AAAI Spring Symposium on Applying Machine Learning to Discourse Processing. 1997: 118-121.
[35]
Vilain M, Burger J, Aberdeen J, et al. A Model-Theoretic Coreference Scoring Scheme[C]// Proceedings of the 6th Conference on Message Understanding Coreference. 1995: 45-52.
[36]
Doddington G R, Mitchell A, Praybocki M A, et al. The Automatic Content Extraction (ACE) Program-Tasks, Data, and Evaluation[C]// Proceedings of the 4th International Conference on Language Resources and Evaluation, 2004: 837-840.
[37]
Weichedel R, Palmer M, Marcus M, et al. Linguistic Data Consortium. OntoNotes Release 5.0 LDC2013T19[DS/OL]. 2013. https://catalog.ldc.upenn.edu/LDC2013T19.
[38]
Bagga A, Baldwin B. Algorithms for Scoring Coreference Chains[C]// Proceedings of the 1st International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference. 1998: 563-566.
[39]
Luo X Q. On Coreference Resolution Performance Metrics[C]// Proceedings of the Conference on Human Language Technology and Conference on Empirical Methods in Natural Language. 2005: 25-32.
[40]
Kingma D P, Ba J. Adam: A Method for Stochastic Optimization[C]// Proceedings of the 3rd International Conference on Learning Representations. 2015: 1-13.
