Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Coreference Resolution: Are the Eliminated Spans Totally Worthless? Read chapter Read first chapter

2021 | OriginalPaper | Chapter

CTRD: A Chinese Theme-Rheme Discourse Dataset

Authors : Biao Fu, Yiqi Tong, Dawei Tian, Yidong Chen, Xiaodong Shi, Ming Zhu

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Discourse topic structure is the key to the cohesion of the discourse and reflects the essence of the text. Current Chinese discourse corpus are constructed mainly based on rhetoric and semantic relations, which ignore the functional information in discourse. To alleviate this problem, we introduce a new Chinese discourse analysis dataset called CTRD, which stands for Chinese Theme-Rheme Discourse dataset. Different from previous discourse banks, CTRD was annotated according to a novel discourse annotation scheme based on the Chinese theme-rheme theory and thematic progression patterns from Halliday’s systemic functional grammar. As a result, we manually annotated 525 news documents from OntoNotes 4.0 with a Kappa value greater than 0.6. And preliminary experiments on this corpus verify the computability of CTRD. Finally, we make CTRD available at https://​github.​com/​ydc/​ctrd.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter ProPC: A Dataset for In-Domain and Cross-Domain Proposition Classification Tasks
next chapter Learning to Select Relevant Knowledge for Neural Machine Translation
Literature
1.
Alekseyenko, N.V.: A corpus-based study of theme and thematic progression in English and Russian non-translated texts and in Russian translated texts. Ph.D. thesis, Kent State University (2013)
2.
3.
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)CrossRef
4.
Cui, L., Wu, Y., Liu, S., Zhang, Y., Zhou, M.: MuTual: a dataset for multi-turn dialogue reasoning. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1406–1416 (2020)
5.
Fang, Y.: A study of topical theme in Chinese: An SFL perspective. In: Meaning in Context: Implementing Intelligent Applications of Language Studies, pp. 84–114. Continuum, London (2008)
6.
Forbes-Riley, K., Webber, B., Joshi, A.: Computing discourse semantics: the predicate-argument semantics of discourse connectives in D-LTAG. J. Semant. 23(1), 55–106 (2006)CrossRef
7.
Halliday, M., Matthiessen, C.M., Matthiessen, C.: An Introduction to Functional Grammar. Routledge (2014)
8.
Jiang, F., Xu, S., Chu, X., Li, P., Zhu, Q., Zhou, G.: MCDTB: a macro-level Chinese discourse treebank. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3493–3504 (2018)
9.
Kizil, M., Kushch, E.: Thematic progression and its types in English literary and legislative texts. Adv. Educ. 6(12), 181–187 (2019)CrossRef
10.
Kong, F., Zhou, G.: A tree kernel-based unified framework for Chinese zero anaphora resolution. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 882–891 (2010)
11.
Krippendorff, K.: Content Analysis: An Introduction to its Methodology. Seikeigeka Orthopedic Surgery (1980)
12.
Lafferty, J.D., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289 (2001)
13.
Li, Y., Feng, W., Sun, J., Kong, F., Zhou, G.: Building Chinese discourse corpus with connective-driven dependency tree structure. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2105–2114 (2014)
14.
Mann, W.C., Thompson, S.A.: Rhetorical structure theory: toward a functional theory of text organization. Text 8(3), 243–281 (1988)
15.
Miculicich, L., Ram, D., Pappas, N., Henderson, J.: Document-level neural machine translation with hierarchical attention networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2947–2954 (2018)
16.
Miltsakaki, E., Prasad, R., Joshi, A.K., Webber, B.L.: The PENN discourse treebank. In: LREC (2004)
17.
Ming, Y.: Rhetorical structure annotation of Chinese news commentaries. J. Chinese Inf. Process. 4 (2008)
18.
Prasad, R., et al.: The PENN discourse treebank 2.0. In: LREC. Citeseer (2008)
19.
Rajpurkar, P., Jia, R., Liang, P.: Know what you don’t know: unanswerable questions for squad. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 784–789 (2018)
20.
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383–2392 (2016)
21.
Rutherford, A., Demberg, V., Xue, N.: A systematic study of neural discourse models for implicit discourse relation. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pp. 281–291 (2017)
22.
Suárez, E.D.O., Cesteros, A.M.F.P.: A new approach for extracting the conceptual schema of texts based on the linguistic thematic progression theory. arXiv preprint arXiv:​2010.​07440 (2020)
23.
Taboada, M., Mann, W.C.: Rhetorical structure theory: looking back and moving ahead. Discourse Stud. 8(3), 423–459 (2006)CrossRef
24.
Tong, Y., Chen, Y., Shi, X.: A multi-task approach for improving biomedical named entity recognition by incorporating multi-granularity information. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 4804–4813. Association for Computational Linguistics, August 2021
25.
Tong, Y., Zheng, J., Zhu, H., Chen, Y., Shi, X.: A document-level neural machine translation model with dynamic caching guided by Theme-Rheme information. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 4385–4395 (2020)
26.
Weischedel, R., et al.: Ontonotes release 4.0. LDC2011T03. Penn.: Linguistic Data Consortium, Philadelphia (2011)
27.
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:​1609.​08144 (2016)
28.
Xi, X.F., Zhou, G.: Building a Chinese discourse topic corpus with a micro-topic scheme based on Theme-Rheme theory. Big Data Anal. 2(1), 9 (2017)
29.
Yan, H., Webster, J.J.: A corpus-based approach to linguistic function. In: Proceedings of the 27th Pacific Asia Conference on Language, Information, and Computation (PACLIC 27), pp. 215–221 (2013)
30.
Yang, J., Zhang, Y.: NCRF++: an open-source neural sequence labeling toolkit. In: Proceedings of ACL 2018, System Demonstrations, pp. 74–79 (2018)
31.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
32.
Yao, Y., et al.: DocRED: a large-scale document-level relation extraction dataset. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 764–777 (2019)
33.
Zhang, M., Song, Y., Qin, B., Liu, T.: Chinese discourse relation recognition. J. Chin. Inf. Process. 27(6), 51 (2013)
34.
Zhou, Y., Xue, N.: The Chinese discourse treebank: a Chinese corpus annotated with discourse relations. Lang. Resour. Eval. 49(2), 397–431 (2015)MathSciNetCrossRef
Metadata
Title
CTRD: A Chinese Theme-Rheme Discourse Dataset
Authors
Biao Fu
Yiqi Tong
Dawei Tian
Yidong Chen
Xiaodong Shi
Ming Zhu
Copyright Year
2021
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-88479-6

Electronic ISBN: 978-3-030-88480-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-88480-2_6

Premium Partner

    Image Credits