Computer Science ›› 2020, Vol. 47 ›› Issue (3): 231-236.doi: 10.11896/jsjkx.190100108

• Artificial Intelligence • Previous Articles     Next Articles

Coreference Resolution Incorporating Structural Information

FU Jian,KONG Fang   

  1. (School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 251006, China)
  • Received:2019-01-15 Online:2020-03-15 Published:2020-03-30
  • About author:FU Jian,born in 1994,postgraduate student.His main research interest include coreference resolution and natural language processing. KONG Fang,born in 1977,doctor.Her main research interest include machine learning,natural language processing,and text analysis.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61876118), Artificial Intelligence Emergency Project (61751206) and National Key Research and Development Plan Sub-project (2017YFB1002101).

Abstract: With the rise and development of deep learning,more and more researchers begin to apply deep learning technology to coreference resolution.However,existing neural coreference resolution models only focus on the sequential information of text and ignore the integration of structural information which has been proved to be very useful in traditional methods.Based on the neural coreference model proposed by Lee et al.,which has the best performance at present,two measures to solve the problem mentioned above with the help of the constituency parse tree were proposed.Firstly,node enumeration was used to replace the original span extraction strategy.It avoids the restriction of span length and reduces the number of spans that don’t satisfy syntactic rules.Secondly,node sequences are obtained through tree traversal,and the features such as height and path are combined to generate the context representation of the constituency parse trees directly.It avoids the problem of missing structural information caused by the use of word and character sequences only.A lot of experiments were conducted on the dataset of CoNLL 2012 Shared Task,and the proposed model achieves 62.35 average F1 for Chinese and 67.24 average F1 for English,which show that the proposed structural information integration strategy can improve the performance of coreference resolution significantly.

Key words: Coreference resolution, Constituency parse tree, Structural information, Height features, Embedding

CLC Number: 

  • TP391
[1]HOBBS J R.Resolving pronoun references[J].Lingua,1978,44(4):311-338.
[2]LAPPIN S,LEASS H J.An algorithm for pronominal anaphora resolution[J].Computational linguistics,1994,20(4):535-561.
[3]MCCORD M C.Slot grammar[M]∥Natural Language and Logic. Berlin:Springer,1990:118-145.
[4]KONG F,ZHOU G D.Pronoun Resolution in English and Chinese Languages Based on Tree Kernel[J].Journal of Software,2012,23(5):1085-1099.
[5]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]∥Advances in Neural Information Processing Systems.Lake Tahoe:NIPS,2013:3111-3119.
[6]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.Austin:EMNLP,2016:2256-2262.
[7]PRADHAN S,MOSCHITTI A,XUE N,et al.CoNLL-2012 shared task:Modeling multilingual unrestricted coreference in OntoNotes[C]∥Joint Conference on EMNLP and CoNLL-Shared Task.Jeju Island:ACL,2012:1-40.
[8]WU J L,MA W Y.A deep learning framework for coreference resolution based on convolutional neural network[C]∥2017 IEEE 11th International Conference on Semantic Computing (ICSC).San Diego:IEEE,2017:61-64.
[9]LEE K,HE L,LEWIS M,et al.End-to-end Neural Coreference Resolution[C]∥Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.Copenhagen:ACL,2017:188-197.
[10]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[11]LEE K,HE L,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.New Orleans:ACL,2018,2:687-692.
[12]PETERS M,NEUMANN M,IYYER M,et al.Deep Contextuali- zed Word Representations[C]∥Proceedings of the 2018 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.New Orleans:ACL,2018:2227-2237.
[13]LIANG D,XU W,ZHAO Y.Combining word-level and character-level representations for relation classification of informal text[C]∥Proceedings of the 2nd Workshop on Representation Learning for NLP.Vancouver:ACL,2017:43-47.
[14]ZHANG X,ZHAO J,LECUN Y.Character-level convolutional networks for text classification[C]∥Advances in Neural Information Processing Systems.Montreal:NIPS,2015:649-657.
[15]LING W,DYER C,BLACK A W,et al.Finding Function in Form:Compositional Character Models for Open Vocabulary Word Representation[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Lisbon:ACL,2015:1520-1530.
[16]VILAIN M,BURGER J,ABERDEEN J,et al.A model-theore- tic coreference scoring scheme[C]∥Proceedings of the 6th Conference on Message Understanding.Columbia:ACL,1995:45-52.
[17]BAGGA A,BALDWIN B.Algorithms for scoring coreference chains[C]∥The First International Conference on Language Resources and Evaluation Workshop on Linguistics Corefe-rence.Granada:LREC,1998,1:563-566.
[18]LUO X.On coreference resolution performance metrics[C]∥ Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing.Vancouver:ACL,2005:25-32.
[19]NAIR V,HINTON G E.Rectified linear units improve restric- ted boltzmann machines[C]∥Proceedings of the 27th International Conference on International Conference on Machine Learning.Haifa:Omni press,2010:807-814.
[20]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958.
[21]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[22]AL-RFOU R,PEROZZI B,SKIENA S.Polyglot:Distributed Word Representations for Multilingual NLP [C]∥Proceedings of the Seventeenth Conference on Computational Natural Language Learning.Sofia:ACL,2013:183-192.
[23]PENNINGTON J,SOCHER R,MANNING C.Glove:Global vectors for word representation[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing (EMNLP).Doha:ACL,2014:1532-1543.
[24]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.Berlin:ACL,2016:643-653.
[25]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.
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