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:
Improving Scholarly Knowledge Representation: Evaluating BERT-Based Models for Scientific Relation Classification Read chapter Read first chapter

2020 | OriginalPaper | Chapter

A Framework for Classifying Temporal Relations with Question Encoder

Authors : Yohei Seki, Kangkang Zhao, Masaki Oguni, Kazunari Sugiyama

Published in: Digital Libraries at Times of Massive Societal Transition

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well because of the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time–event entities) as a filter. We name this module “question encoder.” In our approach, this kind of prior information can extract task-related information from sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques.

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 Improving Scholarly Knowledge Representation: Evaluating BERT-Based Models for Scientific Relation Classification
next chapter When to Use OCR Post-correction for Named Entity Recognition?
Literature
1.
Chambers, N., Cassidy, T., McDowell, B., Bethard, S.: Dense event ordering with a multi-pass architecture. Trans. Assoc. Comput. Linguist. 2, 273–284 (2014)CrossRef
2.
Cheng, F., Miyao, Y.: Classifying temporal relations by bidirectional LSTM over dependency paths. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), pp. 1–6, July 2017
3.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: 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, Minneapolis, MN, USA, pp. 4171–4186, June 2019
4.
Dligach, D., Miller, T., Lin, C., Bethard, S., Savova, G.: Neural temporal relation extraction. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, vol. 2, pp. 746–751, April 2017
5.
Do, H.W., Jeong, Y.S.: Temporal relation classification with deep neural network. In: Proceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp), Hong Kong, China, pp. 454–457, January 2016
6.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
7.
8.
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1746–1751 (2014)
9.
Laokulrat, N., Miwa, M., Tsuruoka, Y., Chikayama, T.: UTTime: temporal relation classification using deep syntactic features. Second Joint Conference on Lexical and Computational Semantics. Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation, Atlanta, Georgia, USA, pp. 88–92, June 2013
10.
Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., Li, J.: Entity-relation extraction as multi-turn question answering. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 1340–1350, July 2019
11.
Lin, C., Dligach, D., Miller, T., Bethard, S., Savova, G.: Multilayered temporal modeling for the clinical domain. J. Am. Med. Inform. Assoc. 23, 387–395 (2016)CrossRef
12.
Lin, C., Miller, T., Dligach, D., Bethard, S., Savova, G.: Representations of time expressions for temporal relation extraction with convolutional neural networks. In: Proceedings of the 2017 Biomedical Natural Language Processing Workshop, pp. 322–327, August 2017. https://​doi.​org/​10.​18653/​v1/​W17-2341
13.
Lin, Z., Feng, M., dos Santos, C.N., Yu, M., Xiang, B., Zhou, B., Bengio, Y.: A structured self-attentive sentence embedding. In: Proceedings of the 5th International Conference on Learning Representations (ICLR 2017) (2017)
14.
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)
15.
Mirza, P., Tonelli, S.: Classifying temporal relations with simple features. In: Proceedings of the 2014 Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), pp. 308–317, April 2014
16.
Mirza, P., Tonelli, S.: On the contribution of word embeddings to temporal relation classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, pp. 2818–2828. The COLING 2016 Organizing Committee, December 2016
17.
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1532–1543, October 2014
18.
Tourille, J., Ferret, O., Névéol, A., Tannier, X.: Neural architecture for temporal relation extraction: a bi-LSTM approach for detecting narrative containers. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 224–230, July 2017. https://​doi.​org/​10.​18653/​v1/​P17-2035
19.
Wang, Y., et al.: Clinical information extraction applications: a literature review. J. Biomed. Inform. 77, 34–49 (2018)CrossRef
20.
Zhang, Y., Li, P., Zhou, G.: Classifying temporal relations between events by deep BiLSTM. In: Proceedings of the 2018 International Conference on Asian Language Processing (IALP 2018), pp. 267–272, November 2018
21.
Zheng, N., et al.: Predicting COVID-19 in China using hybrid AI model. IEEE Trans. Cybern. 50(7), 2891–2904 (2020)CrossRef
Metadata
Title
A Framework for Classifying Temporal Relations with Question Encoder
Authors
Yohei Seki
Kangkang Zhao
Masaki Oguni
Kazunari Sugiyama
Copyright Year
2020
Book
Digital Libraries at Times of Massive Societal Transition

Print ISBN: 978-3-030-64451-2

Electronic ISBN: 978-3-030-64452-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-64452-9_2

Premium Partner

    Image Credits