research-article

Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank

Authors:

Shexia HeAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 21, Issue 6

Article No.: 130, Pages 1 - 16

https://doi.org/10.1145/3526214

Published: 12 November 2022 Publication History

Abstract

The latest developments in neural semantic role labeling (SRL) have shown great performance improvements with both the dependency and span formalism/styles. Although the two styles share many similarities in linguistic meaning and computation, most previous studies focus on a single style. In this article, we define a new cross-style semantic role label convention and propose a new cross-style joint optimization model designed around the most basic linguistic meaning of a semantic role. Our work provides a solution to make the results of the two styles more comparable and allowing both formalisms of SRL to benefit from their natural connections in both linguistics and computation. Our model learns a general semantic argument structure and is capable of outputting in either style. Additionally, we propose a syntax-aided method to uniformly enhance the learning of both dependency and span representations. Experiments show that the proposed methods are effective on both span and dependency SRL benchmarks.

References

[1]

Jiaxun Cai, Shexia He, Zuchao Li, and Hai Zhao. 2018. A full end-to-end semantic role labeler, syntactic-agnostic over syntactic-aware?. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, NM, 2753–2765. https://www.aclweb.org/anthology/C18-1233.

[2]

Xavier Carreras and Lluís Màrquez. 2004. Introduction to the CoNLL-2004 shared task: Semantic role labeling. In Proceedings of the 8th Conference on Computational Natural Language Learning (CoNLL’04) at HLT-NAACL 2004. Association for Computational Linguistics, Boston, MA, 89–97. https://www.aclweb.org/anthology/W04-2412.

[3]

Xavier Carreras and Lluís Màrquez. 2005. Introduction to the CoNLL-2005 shared task: Semantic role labeling. In Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL’05). Association for Computational Linguistics, Ann Arbor, MI, 152–164. https://www.aclweb.org/anthology/W05-0620.

[4]

Noam Chomsky. 1993. Lectures on Government and Binding: The Pisa Lectures. Walter de Gruyter.

[5]

Michael Collins. 2003. Head-driven statistical models for natural language parsing. Computational Linguistics 29, 4 (2003), 589–637. https://doi.org/10.1162/089120103322753356

Digital Library

[6]

Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC’06). European Language Resources Association (ELRA), Genoa, Italy. http://www.lrec-conf.org/proceedings/lrec2006/pdf/440_pdf.pdf.

[7]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. 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). Association for Computational Linguistics, Minneapolis, MN, 4171–4186. https://doi.org/10.18653/v1/N19-1423

[8]

Timothy Dozat and Christopher D. Manning. 2017. Deep biaffine attention for neural dependency parsing. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=Hk95PK9le.

[9]

Nicholas FitzGerald, Oscar Täckström, Kuzman Ganchev, and Dipanjan Das. 2015. Semantic role labeling with neural network factors. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, 960–970. https://doi.org/10.18653/v1/D15-1112

[10]

W. Nelson Francis and Henry Kucera. 1979. Brown corpus manual: Manual of information to accompany a standard corpus of present-day edited American English for use with digital computers. Brown University, Providence, Rhode Island, USA.

[11]

Jan Hajič, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martí, Lluís Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Štěpánek, Pavel Straňák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang. 2009. The CoNLL-2009 shared task: Syntactic and semantic dependencies in multiple languages. In Proceedings of the 13th Conference on Computational Natural Language Learning (Co’09): Shared Task. Association for Computational Linguistics, Boulder, CO, 1–18. https://aclanthology.org/W09-1201.

[12]

Luheng He, Kenton Lee, Omer Levy, and Luke Zettlemoyer. 2018. Jointly predicting predicates and arguments in neural semantic role labeling. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Melbourne, Australia, 364–369. https://doi.org/10.18653/v1/P18-2058

[13]

Luheng He, Kenton Lee, Mike Lewis, and Luke Zettlemoyer. 2017. Deep semantic role labeling: What works and what’s next. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 473–483. https://doi.org/10.18653/v1/P17-1044

[14]

Shexia He, Zuchao Li, Hai Zhao, and Hongxiao Bai. 2018. Syntax for semantic role labeling, to be, or not to be. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 2061–2071. https://doi.org/10.18653/v1/P18-1192

[15]

Richard Johansson and Pierre Nugues. 2007. Extended constituent-to-dependency conversion for English. In Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA’07). University of Tartu, Estonia, Tartu, Estonia, 105–112. https://www.aclweb.org/anthology/W07-2416.

[16]

Richard Johansson and Pierre Nugues. 2008. Dependency-based syntactic–semantic analysis with PropBank and NomBank. In CoNLL 2008: Proceedings of the 12th Conference on Computational Natural Language Learning. Coling 2008 Organizing Committee, Manchester, England, 183–187. https://www.aclweb.org/anthology/W08-2123.

[17]

Nikita Kitaev and Dan Klein. 2018. Constituency parsing with a self-attentive encoder. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Melbourne, Australia, 2676–2686. https://doi.org/10.18653/v1/P18-1249

[18]

Zuchao Li, Shexia He, Jiaxun Cai, Zhuosheng Zhang, Hai Zhao, Gongshen Liu, Linlin Li, and Luo Si. 2018. A unified syntax-aware framework for semantic role labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 2401–2411. https://doi.org/10.18653/v1/D18-1262

[19]

Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, and Xiang Zhou. 2019. Dependency or span, end-to-end uniform semantic role labeling. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, January 27 - February 1, 2019. AAAI Press, 6730–6737. https://doi.org/10.1609/aaai.v33i01.33016730

[20]

David M. Magerman. 1994. Natural language parsing as statistical pattern recognition. CoRR abs/cmp-lg/9405009 (1994). arXiv:cmp-lg/9405009 http://arxiv.org/abs/cmp-lg/9405009.

[21]

Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017. A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. In Proceedings of the 21st Conference on Computational Natural Language Learning (Co’17). Association for Computational Linguistics, Vancouver, Canada, 411–420. https://doi.org/10.18653/v1/K17-1041

[22]

Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 1506–1515. https://doi.org/10.18653/v1/D17-1159

[23]

Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19, 2 (1993), 313–330. https://www.aclweb.org/anthology/J93-2004.

Digital Library

[24]

Adam Meyers, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young, and Ralph Grishman. 2004. The NomBank project: An interim report. In Proceedings of the Workshop Frontiers in Corpus Annotation at HLT-NAACL 2004. Association for Computational Linguistics, Boston, MA, 24–31. https://aclanthology.org/W04-2705.

[25]

Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles. Computational Linguistics 31, 1 (2005), 71–106. https://doi.org/10.1162/0891201053630264

Digital Library

[26]

Hao Peng, Sam Thomson, Swabha Swayamdipta, and Noah A. Smith. 2018. Learning joint semantic parsers from disjoint data. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, LA, 1492–1502. https://doi.org/10.18653/v1/N18-1135

[27]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). Association for Computational Linguistics, Doha, Qatar, 1532–1543. https://doi.org/10.3115/v1/D14-1162

[28]

Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Association for Computational Linguistics, New Orleans, LA, 2227–2237. https://doi.org/10.18653/v1/N18-1202

[29]

Sameer Pradhan, Wayne Ward, Kadri Hacioglu, James Martin, and Daniel Jurafsky. 2005. Semantic role labeling using different syntactic views. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05). Association for Computational Linguistics, Ann Arbor, M, 581–588. https://doi.org/10.3115/1219840.1219912

Digital Library

[30]

Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2005. The necessity of syntactic parsing for semantic role labeling. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05), Edinburgh, Scotland, UK, July 30 - August 5, 2005, Leslie Pack Kaelbling and Alessandro Saffiotti (Eds.). Professional Book Center, 1117–1123. http://ijcai.org/Proceedings/05/Papers/1672.pdf.

[31]

Vasin Punyakanok, Dan Roth, and Wen-tau Yih. 2008. The importance of syntactic parsing and inference in semantic role labeling. Computational Linguistics 34, 2 (2008), 257–287. https://doi.org/10.1162/coli.2008.34.2.257

Digital Library

[32]

Michael Roth and Mirella Lapata. 2016. Neural semantic role labeling with dependency path embeddings. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Berlin, Germany, 1192–1202. https://doi.org/10.18653/v1/P16-1113

[33]

Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018. Linguistically-informed self-attention for semantic role labeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 5027–5038. https://doi.org/10.18653/v1/D18-1548

[34]

Mihai Surdeanu, Richard Johansson, Adam Meyers, Lluís Màrquez, and Joakim Nivre. 2008. The CoNLL 2008 shared task on joint parsing of syntactic and semantic dependencies. In CoNLL 2008: Proceedings of the12th Conference on Computational Natural Language Learning. Coling 2008 Organizing Committee, Manchester, England, 159–177. https://aclanthology.org/W08-2121.

[35]

Nianwen Xue and Martha Palmer. 2004. Calibrating features for semantic role labeling. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Barcelona, Spain, 88–94. https://www.aclweb.org/anthology/W04-3212.

[36]

Hiroyasu Yamada and Yuji Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proceedings of the 8th International Conference on Parsing Technologies. Nancy, France, 195–206. https://www.aclweb.org/anthology/W03-3023.

[37]

Jie Zhou and Wei Xu. 2015. End-to-end learning of semantic role labeling using recurrent neural networks. In 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). Association for Computational Linguistics, Beijing, China, 1127–1137. https://doi.org/10.3115/v1/P15-1109

Cited By

Singh SSrivastava VKumar ATiwari SSingh DLee H(2023)Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and ApplicationsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353973222:5(1-47)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3539732
Hailu BAssabie YSinshaw Y(2023)Semantic Role Labeling for Amharic Text Using Multiple Embeddings and Deep Neural NetworkIEEE Access10.1109/ACCESS.2023.326314711(33274-33295)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3263147
Chen XChen ZXiao LZhou M(2022)A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis TechnologyComputational Intelligence and Neuroscience10.1155/2022/20966342022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2096634

Index Terms

Dependency and Span, Cross-Style Semantic Role Labeling on PropBank and NomBank
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Lexical semantics

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 21, Issue 6

November 2022

372 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3568970

Issue’s Table of Contents

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Publication History

Published: 12 November 2022

Online AM: 13 April 2022

Accepted: 12 March 2022

Revised: 07 December 2021

Received: 15 November 2020

Published in TALLIP Volume 21, Issue 6

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Cited By

Singh SSrivastava VKumar ATiwari SSingh DLee H(2023)Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and ApplicationsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/353973222:5(1-47)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3539732
Hailu BAssabie YSinshaw Y(2023)Semantic Role Labeling for Amharic Text Using Multiple Embeddings and Deep Neural NetworkIEEE Access10.1109/ACCESS.2023.326314711(33274-33295)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3263147
Chen XChen ZXiao LZhou M(2022)A Novel Sentiment Analysis Model of Museum User Experience Evaluation Data Based on Unbalanced Data Analysis TechnologyComputational Intelligence and Neuroscience10.1155/2022/20966342022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/2096634

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