Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-18T10:08:14.230Z Has data issue: false hasContentIssue false
  • English
  • Français

A survey of the extraction and applications of causal relations

Published online by Cambridge University Press:  20 January 2022

Brett Drury Open the ORCID record for Brett Drury [Opens in a new window] ,
Hugo Gonçalo Oliveira Open the ORCID record for Hugo Gonçalo Oliveira [Opens in a new window]  and
Alneu de Andrade Lopes
Brett Drury*
Affiliation:
LIAAD INESC Tec, R. Dr. Roberto Frias, Porto, Portugal
Hugo Gonçalo Oliveira
Affiliation:
CISUC, Department of Informatics Engineering, Universidade de Coimbra, Coimbra, Portugal
Alneu de Andrade Lopes
Affiliation:
ICMC, USP Av. Trab. São Carlense, São Paulo, Brazil
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Causationin written natural language can express a strong relationship between events and facts. Causation in the written form can be referred to as a causal relation where a cause event entails the occurrence of an effect event. A cause and effect relationship is stronger than a correlation between events, and therefore aggregated causal relations extracted from large corpora can be used in numerous applications such as question-answering and summarisation to produce superior results than traditional approaches. Techniques like logical consequence allow causal relations to be used in niche practical applications such as event prediction which is useful for diverse domains such as security and finance. Until recently, the use of causal relations was a relatively unpopular technique because the causal relation extraction techniques were problematic, and the relations returned were incomplete, error prone or simplistic. The recent adoption of language models and improved relation extractors for natural language such as Transformer-XL (Dai et al. (2019). Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860) has seen a surge of research interest in the possibilities of using causal relations in practical applications. Until now, there has not been an extensive survey of the practical applications of causal relations; therefore, this survey is intended precisely to demonstrate the potential of causal relations. It is a comprehensive survey of the work on the extraction of causal relations and their applications, while also discussing the nature of causation and its representation in text.

Keywords

Causal relationsSurveySentimentEvent predictionInformation retrievalCause identification
Type
Survey Paper
Information
Natural Language Engineering , Volume 28 , Issue 3 , May 2022 , pp. 361 - 400
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abinesh, S. (2017). Prediction of Future News Trends. Master’s Thesis, National University of Ireland Galway, NUIG, University Road, Galway, Ireland.Google Scholar
Ackerman, E.J.M. (2012). Extracting a causal network of news topics. In Herrero, P., Panetto, H., Meersman, R. and Dillon, T. (eds), On the Move to Meaningful Internet Systems: OTM 2012 Workshops. Lecture Notes in Computer Science, vol. 7567. Springer, pp. 33–42.Google Scholar
Ackerman, E.J.M. (2013). Extracting Causal Relations between News Topics from Distributed Sources. PhD Thesis, Technische Universität Dresden.Google Scholar
Adams, F.C. (2008). Stars in other universes: stellar structure with different fundamental constants. Journal of Cosmology and Astroparticle Physics 2008(08), 010.CrossRefGoogle Scholar
Agueda, C.P. (2010). Extraction and Analysis of Conditional and Causal Sentences for Information Retrieval. PhD Thesis, Universidad Pontificia Comillas.Google Scholar
Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S. and Vollgraf, R. (2019a). FLAIR: an easy-to-use framework for state-of-the-art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), Minneapolis, Minnesota. Association for Computational Linguistics, pp. 54–59.Google Scholar
Akbik, A., Bergmann, T., Blythe, D., Rasul, K., Schweter, S. and Vollgraf, R. (2019b). Flair: an easy-to-use framework for state-of-the-art NLP. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pp. 54–59.Google Scholar
Akl, H.A., Mariko, D. and Labidurie, E. (2020). Semeval-2020 task 5: detecting counterfactuals by disambiguation. arXiv preprint arXiv:2005.08519.Google Scholar
Aliseda, A. (2006). Abductive Reasoning, vol. 330. Berlin, Germany: Springer.Google Scholar
Altenberg, B. (1984). Causal linking in spoken and written English. Studia Linguistica 38(1), 20–69.CrossRefGoogle Scholar
Bakal, G., Talari, P., Kakani, E.V. and Kavuluru, R. (2018). Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations. Journal of Biomedical Informatics 82, 189–199.CrossRefGoogle ScholarPubMed
Barik, B., Marsi, E. and Öztürk, P. (2017). Extracting Causal Relations Among Complex Events in Natural Science Literature. Cham: Springer International Publishing, pp. 131–137.Google Scholar
Barrow, J.D. and Silk, J. (1980). The structure of the early universe. Scientific American 242(4), 118–129.CrossRefGoogle Scholar
Beebee, H., Hitchcock, C. and Menzies, P. (2015). The Oxford Handbook of Causation. OUP.Google Scholar
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F. and Vaughan, J.W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.CrossRefGoogle Scholar
Bhaskoro, S.B., Akbar, S. and Supangkat, S.H. (2015). Identification of causal pattern using opinion analysis in Indonesian medical texts. In 2015 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, pp. 1–7.CrossRefGoogle Scholar
Binh Tran, G. (2013). Structured summarization for news events. In Proceedings of the 22nd International Conference on World Wide Web. ACM, pp. 343–348.CrossRefGoogle Scholar
Blanco, E., Castell, N. and Moldovan, D.I. (2008). Causal relation extraction. In LREC.Google Scholar
Bui, Q.-C., Nualláin, B.Ó., Boucher, C.A. and Sloot, P.M. (2010). Extracting causal relations on HIV drug resistance from literature. BMC Bioinformatics 11(1), 101.CrossRefGoogle Scholar
Cao, M., Sun, X. and Zhuge, H. (2016). The role of cause-effect link within scientific paper. In 2016 12th International Conference on Semantics, Knowledge and Grids (SKG). IEEE, pp. 32–39.CrossRefGoogle Scholar
Cao, M., Sun, X. and Zhuge, H. (2018). The contribution of cause-effect link to representing the core of scientific paper—the role of semantic link network. PloS One 13(6), e0199303.CrossRefGoogle ScholarPubMed
Cao, Y., Cao, C., Zhang, J. and Niu, W. (2015). Two-phased event causality acquisition: coupling the boundary identification and argument identification approaches. In International Conference on Knowledge Science, Engineering and Management. Springer, pp. 588–599.CrossRefGoogle Scholar
Cao, Y., Zhang, P., Guo, J. and Guo, L. (2014). Mining large-scale event knowledge from web text. Procedia Computer Science 29(0), 478–487. 2014 International Conference on Computational Science.CrossRefGoogle Scholar
Cao, Y.-N., Cao, C., Wang, S. and Zang, L. (2012). Web mining for causal relations between events. Information 15(1), 427–434.Google Scholar
Casati, R. and Varzi, A. (2020). Events. In Zalta, E.N. (ed.), The Stanford Encyclopedia of Philosophy, summer 2020 Edn. Metaphysics Research Lab, Stanford University.Google Scholar
Caselli, T. and Vossen, P. (2017). The event storyline corpus: a new benchmark for causal and temporal relation extraction. In Proceedings of the Events and Stories in the News Workshop, pp. 77–86.CrossRefGoogle Scholar
Chan, K. and Lam, W. (2005). Extracting causation knowledge from natural language texts. The International Journal of Intelligent Systems 20(3), 327–358.CrossRefGoogle Scholar
Chen, Y., Hou, W., Li, S., Wu, C. and Zhang, X. (2020). End-to-end emotion-cause pair extraction with graph convolutional network. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 198–207.CrossRefGoogle Scholar
Chen, Y., Lee, S.Y.M., Li, S. and Huang, C.-R. (2010). Emotion cause detection with linguistic constructions. In Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, pp. 179–187.Google Scholar
Cole, S., Royal, M., Valtorta, M., Huhns, M. and Bowles, J. (2006). A lightweight tool for automatically extracting causal relationships from text. In SoutheastCon, 2006. Proceedings of the IEEE, pp. 125–129.CrossRefGoogle Scholar
Collins, J.D., Hall, E.J. and Paul, L.A. (2004). Causation and Counterfactuals. MIT Press.Google Scholar
Common, (2021). Common Crawl. Available at http://commoncrawl.org/ (accessed 22 March 2021).Google Scholar
Copley, B. and Martine, F. (eds.) (2015). Causation in Grammatical Structures, vol. 1. OUP.Google Scholar
Copley, B. and Wolf, P. (2015). Theories of Causation should inform linguistic theory and vice versa. In Causation in Grammatical Structures, vol. 1. OUP.Google Scholar
Dai, Z., Yang, Z., Yang, Y., Cohen, W.W., Carbonell, J., Le, Q.V. and Salakhutdinov, R. (2019). Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860.Google Scholar
Danescu-Niculescu-Mizil, C. and Lee, L. (2011). Chameleons in imagined conversations: a new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics.Google Scholar
Dasgupta, T., Saha, R., Dey, L. and Naskar, A. (2018). Automatic extraction of causal relations from text using linguistically informed deep neural networks. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pp. 306–316.CrossRefGoogle Scholar
Datta, S., Ganguly, D., Roy, D., Bonin, F., Jochim, C. and Mitra, M. (2020a). Retrieving potential causes from a query event. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1689–1692.CrossRefGoogle Scholar
Datta, S., Ganguly, D., Roy, D., Greene, D., Jochim, C. and Bonin, F. (2020b). Overview of the causality-driven adhoc information retrieval (cair) task at fire-2020. In Forum for Information Retrieval Evaluation, pp. 14–17.CrossRefGoogle Scholar
Degand, L. (1994). Towards an account of causation in a multilingual text generation system. In Proceedings of the Seventh International Workshop on Natural Language Generation, INLG’94, pp. 108–116.CrossRefGoogle Scholar
Degand, L. (2000). Causal connectives or causal prepositions? discursive constraints. Journal of Pragmatics 32(6), 687–707.CrossRefGoogle Scholar
Dehkharghani, R., Mercan, H., Javeed, A. and Saygin, Y. (2014). Sentimental causal rule discovery from twitter. Expert Systems with Applications 41(10), 4950–4958.CrossRefGoogle Scholar
Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (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), Minneapolis, Minnesota. Association for Computational Linguistics, pp. 4171–4186.Google Scholar
de Spinoza, B. (1996). The Ethics. Penguin.Google Scholar
Ding, X., Zhang, Y., Liu, T. and Duan, J. (2014). Using structured events to predict stock price movement: an empirical investigation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1415–1425.CrossRefGoogle Scholar
Do, Q.X., Chan, Y.S. and Roth, D. (2011). Minimally supervised event causality identification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP’11, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 294–303.Google Scholar
Drury, B. and de Andrade Lopes, A. (2015). The identification of indicators of sentiment using a multi-view self-training algorithm. Oslo Studies in Language 7(1), 379–395.CrossRefGoogle Scholar
Drury, B., Rocha, C., Moura, M.-F. and de Andrade Lopes, A. (2016). The extraction from news stories a causal topic centred Bayesian graph for sugarcane. In Proceedings of the 20th International Database Engineering & Applications Symposium. ACM, pp. 364–369.CrossRefGoogle Scholar
Dunietz, J., Levin, L. and Carbonell, J. (2015). Annotating causal language using corpus lexicography of constructions. In The 9th Linguistic Annotation Workshop held in Conjunction with NAACL 2015, vol. 188.CrossRefGoogle Scholar
Dunietz, J., Levin, L. and Carbonell, J. (2017). The BECauSE corpus 2.0: annotating causality and overlapping relations. In LAW XI 2017, vol. 95.Google Scholar
Eisenschlos, J., Ruder, S., Czapla, P., Kadras, M., Gugger, S. and Howard, J. (2019). Multifit: efficient multi-lingual language model fine-tuning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5706–5711.CrossRefGoogle Scholar
Fajcik, M., Jon, J., Docekal, M. and Smrz, P. (2020). But-fit at semeval-2020 task 5: automatic detection of counterfactual statements with deep pre-trained language representation models. arXiv preprint arXiv:2007.14128.Google Scholar
Girju, R. (2003). Automatic detection of causal relations for question answering. In Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering - Volume 12. Association for Computational Linguistics, pp. 76–83.CrossRefGoogle Scholar
Girju, R. and Moldovan, D. (2002). Mining answers for causation questions. In AAAI Symposium.Google Scholar
Gordon, A.S., Kozareva, Z. and Roemmele, M. (2012). Semeval-2012 task 7: choice of plausible alternatives: an evaluation of commonsense causal reasoning. In Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, SemEval’12, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 394–398.Google Scholar
Grivaz, C. (2012). Automatic Extraction of Causal Knowledge from Natural Language Texts. PhD Thesis, University of Geneva.Google Scholar
Gui, L., Xu, R., Lu, Q., Wu, D. and Zhou, Y. (2016). Emotion cause extraction, a challenging task with corpus construction. In Chinese National Conference on Social Media Processing. Springer, pp. 98–109.CrossRefGoogle Scholar
Gui, L., Yuan, L., Xu, R., Liu, B., Lu, Q. and Zhou, Y. (2014). Emotion cause detection with linguistic construction in Chinese Weibo text. In Natural Language Processing and Chinese Computing. Springer, pp. 457–464.CrossRefGoogle Scholar
Hall, N. and Paul, L. (2013). Causation: A User’s Guide. Oxford: Oxford University Press.Google Scholar
Hashimoto, C., Torisawa, K., Kloetzer, J. and Oh, J.-H. (2015). Generating event causality hypotheses through semantic relations. In AAAI, pp. 2396–2403.Google Scholar
Hashimoto, C., Torisawa, K., Kloetzer, J., Sano, M., Varga, I., Oh, J.-H. and Kidawara, Y. (2014). Toward future scenario generation: extracting event causality exploiting semantic relation, context, and association features. In ACL (1), pp. 987–997.CrossRefGoogle Scholar
Hassanzadeh, O., Bhattacharjya, D., Feblowitz, M., Srinivas, K., Perrone, M., Sohrabi, S. and Katz, M. (2020). Causal knowledge extraction through large-scale text mining. In AAAI, pp. 13610–13611.CrossRefGoogle Scholar
Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Ó Séaghdha, D., Padó, S., Pennacchiotti, M., Romano, L. and Szpakowicz, S. (2009). Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals. In Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions, SEW’09, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 94–99.CrossRefGoogle Scholar
Hidey, C. and McKeown, K. (2016). Identifying causal relations using parallel Wikipedia articles. In Proceedings of the Association of Computational Linguistics.CrossRefGoogle Scholar
Higashinaka, R. and Isozaki, H. (2008a). Automatically acquiring causal expression patterns from relation-annotated corpora to improve question answering for why-questions. ACM Transactions on Asian Language Information Processing (TALIP) 7(2), 6.CrossRefGoogle Scholar
Higashinaka, R. and Isozaki, H. (2008b). Corpus-based question answering for why-questions. In IJCNLP, pp. 418–425.Google Scholar
Hitchcock, C.R. (1995). The mishap at Reichenbach fall: singular vs. general causation. Philosophical Studies 78(3), 257–291.CrossRefGoogle Scholar
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation 9(8), 1735–1780.CrossRefGoogle ScholarPubMed
Hu, Z., Rahimtoroghi, E. and Walker, M. (2017). Inference of fine-grained event causality from blogs and films. In Proceedings of the Events and Stories in the News Workshop, Vancouver, Canada. Association for Computational Linguistics, pp. 52–58.CrossRefGoogle Scholar
Ishii, H., Ma, Q. and Yoshikawa, M. (2010a). Causal network construction to support understanding of news. In HICSS, pp. 1–10.CrossRefGoogle Scholar
Ishii, H., Ma, Q. and Yoshikawa, M. (2010b). Causal network construction to support understanding of news. In Proceedings of the Annual Hawaii International Conference on System Sciences.CrossRefGoogle Scholar
Ittoo, A. and Bouma, G. (2011a). Extracting explicit and implicit causal relations from sparse, domain-specific texts. In International Conference on Application of Natural Language to Information Systems. Springer, pp. 52–63.Google Scholar
Ittoo, A. and Bouma, G. (2011b). Extracting explicit and implicit causal relations from sparse, domain-specific texts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 6716, pp. 52–63.CrossRefGoogle Scholar
Izumi, K. and Sakaji, H. (2019). Economic causal-chain search using text mining technology. In Proceedings of the First Workshop on Financial Technology and Natural Language Processing, pp. 61–65.Google Scholar
Jain, L.C. and Medsker, L.R. (1999). Recurrent Neural Networks: Design and Applications, 1st Edn. Boca Raton, FL, USA: CRC Press, Inc.Google Scholar
Jastrzebski, S., Lesniak, D. and Czarnecki, W.M. (2017). How to evaluate word embeddings? on importance of data efficiency and simple supervised tasks. CoRR, abs/1702.02170.Google Scholar
Jensen, F.V. (2001). Causal and Bayesian networks. In Bayesian Networks and Decision Graphs. Springer, pp. 3–34.CrossRefGoogle Scholar
Jin, X., Wang, X., Luo, X., Huang, S. and Gu, S. (20200. Inter-sentence and implicit causality extraction from chinese corpus. In Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K. and Pan, S.J. (eds), Advances in Knowledge Discovery and Data Mining. Cham: Springer International Publishing, pp. 739–751.Google Scholar
Joskowicz, L., Ksiezyck, T. and Grishman, R. (1989). Deep domain models for discourse analysis. In AI Systems in Government Conference, 1989. Proceedings of the Annual, pp. 195–200.CrossRefGoogle Scholar
Kaneko, K. and Bekki, D. (2014a). Building a japanese corpus of temporal-causal-discourse structures based on sdrt for extracting causal relations. In Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL), pp. 33–39.CrossRefGoogle Scholar
Kaneko, K. and Bekki, D. (2014b). Toward a discourse theory for annotating causal relations in japanese. In Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing, pp. 460–469.Google Scholar
Kang, D., Gangal, V., Lu, A., Chen, Z. and Hovy, E.H. (20170. Detecting and explaining causes from text for a time series event. In Palmer, M., Hwa, R. and Riedel, S. (eds), Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9–11, 2017. Association for Computational Linguistics, pp. 2758–2767.CrossRefGoogle Scholar
Khoo, C., Chan, S. and Niu, Y. (2002). The many facets of the cause-effect relation. In Green, R., Bean, C. and Myaeng, S. (eds), The Semantics of Relationships. Information Science and Knowledge Management, vol. 3. Netherlands: Springer, pp. 51–70.CrossRefGoogle Scholar
Khoo, C.S., Myaeng, S.H. and Oddy, R.N. (2001). Using cause-effect relations in text to improve information retrieval precision. Information Processing & Management 37(1), 119–145.CrossRefGoogle Scholar
Khoo, C.S.-G. (1996). Automatic Identification of Causal Relations in Text and their Use for Improving Precision in Information Retrieval. PhD Thesis, Syracuse University.Google Scholar
Kilicoglu, H. (2016). Inferring implicit causal relationships in biomedical literature. In Proceedings of the 15th Workshop on Biomedical Natural Language Processing, pp. 46–55.CrossRefGoogle Scholar
Kim, H.D., Castellanos, M., Hsu, M., Zhai, C., Rietz, T. and Diermeier, D. (2013). Mining causal topics in text data: iterative topic modeling with time series feedback. In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, CIKM’13, New York, NY, USA. ACM, pp. 885–890.CrossRefGoogle Scholar
Kim, H.D., Zhai, C., Rietz, T.A., Diermeier, D., Hsu, M., Castellanos, M. and Ceja Limon, C.A. (2012). Incatomi: integrative causal topic miner between textual and non-textual time series data. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM’12, New York, NY, USA. ACM, pp. 2689–2691.CrossRefGoogle Scholar
Kolomiyets, O. and Moens, M.-F. (2011). A survey on question answering technology from an information retrieval perspective. Information Sciences 181(24), 5412–5434.CrossRefGoogle Scholar
Krishnan, A., Sligh, J., Tinsley, E., Crohn, N., Bandos, J., Bush, H., Depasquale, J. and Palakal, M. (2014). Causal association mining from geriatric literature. In 2014 IEEE International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, pp. 226–230.CrossRefGoogle Scholar
Kumar, S. (2017). A survey of deep learning methods for relation extraction. arXiv preprint arXiv:1705.03645.Google Scholar
Kunneman, F.A. and van den Bosch, A. (2012). Leveraging unscheduled event prediction through mining scheduled event tweets. In 24th Benelux Conference on Artificial Intelligence. Maastricht:[sn].Google Scholar
Kyriakakis, M., Androutsopoulos, I., Saudabayev, A. and Ginés i Ametllé, J. (2019). Transfer learning for causal sentence detection. In Proceedings of the 18th BioNLP Workshop and Shared Task, Florence, Italy. Association for Computational Linguistics, pp. 292–297.CrossRefGoogle Scholar
Lee, S.Y.M., Chen, Y. and Huang, C.-R. (2010). A text-driven rule-based system for emotion cause detection. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Association for Computational Linguistics, pp. 45–53.Google Scholar
Lee, S.Y.M., Chen, Y., Huang, C.-R. and Li, S. (2013). Detecting emotion causes with a linguistic rule-based approach. Computational Intelligence 29(3), 390–416.CrossRefGoogle Scholar
Levin, B. (1986). Causation: The perspective from resultatives. In The New York Times, vol. 8.Google Scholar
Levin, B. (1993). English Verb Classes and Alternations. University of Chicago Press.Google Scholar
Li, W., Wu, M., Lu, Q., Xu, W. and Yuan, C. (2006). Extractive summarization using inter-and intra-event relevance. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 369–376.CrossRefGoogle Scholar
Li, W. and Xu, H. (2014). Text-based emotion classification using emotion cause extraction. Expert Systems with Applications 41(4), 1742–1749.CrossRefGoogle Scholar
Li, X., Xie, H., Chen, L., Wang, J. and Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems 69, 14–23.CrossRefGoogle Scholar
Li, Z., Ding, X. and Liu, T. (2018). Constructing narrative event evolutionary graph for script event prediction. In Proceedings of the IJCAI 2018.CrossRefGoogle Scholar
Li, Z., Li, Q., Zou, X. and Ren, J. (2019). Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings. arXiv preprint arXiv:1904.07629.Google Scholar
Li, Z., Li, Q., Zou, X. and Ren, J. (2021). Causality extraction based on self-attentive biLSTM-CRF with transferred embeddings. Neurocomputing 423, 207–219.CrossRefGoogle Scholar
Liu, J., Chen, Y. and Zhao, J. (2020). Knowledge enhanced event causality identification with mention masking generalizations. In Bessiere, C. (ed), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. International Joint Conferences on Artificial Intelligence Organization, pp. 3608–3614.CrossRefGoogle Scholar
Liu, X., He, P., Chen, W. and Gao, J. (2019). Multi-task deep neural networks for natural language understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4487–4496.CrossRefGoogle Scholar
Lorenz, G. (1999). Learning to cohere: causal links in native vs. non-native argumentative writing. In Pragmatics and Beyond New Series, pp. 55–76.CrossRefGoogle Scholar
Luo, Z., Sha, Y., Zhu, K.Q., Hwang, S.-w. and Wang, Z. (2016). Commonsense causal reasoning between short texts. In KR, pp. 421–431.Google Scholar
Mackie, J.L. (1965). Causes and conditions. American Philosophical Journal 2, 245–255.Google Scholar
Mackie, J.L. (1974). The Cement of the Universe: A Study of Causation. Oxford: Clarendon Press.Google Scholar
Maekawa, K., Yamazaki, M., Ogiso, T., Maruyama, T., Ogura, H., Kashino, W., Koiso, H., Yamaguchi, M., Tanaka, M. and Den, Y. (2014). Balanced corpus of contemporary written japanese. Language Resources and Evaluation 48(2), 345–371.CrossRefGoogle Scholar
Marcu, D. and Echihabi, A. (2002). An unsupervised approach to recognizing discourse relations. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL’02, Stroudsburg, PA, USA. Association for Computational Linguistics, pp. 368–375.Google Scholar
Mariko, D., Akl, H.A., Labidurie, E., Durfort, S., De Mazancourt, H. and El-Haj, M. (2020). Financial document causality detection shared task (fincausal 2020). arXiv preprint arXiv:2012.02505.Google Scholar
Mehrabi, S., Krishnan, A., Tinsley, E., Sligh, J., Crohn, N., Bush, H., Depasquale, J., Bandos, J. and Palakal, M. (2013). Event causality identification using conditional random field in geriatric care domain. In Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013, vol. 1, pp. 339–343.CrossRefGoogle Scholar
Mellor, D. (1998). The Facts of Causation . International Library of Philosophy, Psychology, and Scientific Method. Routledge.Google Scholar
Mihaila, C. and Ananiadou, S. (2013). What causes a causal relation? detecting causal triggers in biomedical scientific discourse. In ACL (Student Research Workshop), pp. 38–45.Google Scholar
Mihăilă, C. and Ananiadou, S. (2014). Semi-supervised learning of causal relations in biomedical scientific discourse. Biomedical Engineering Online 13(Suppl 2), S1.CrossRefGoogle Scholar
Mikolov, T., Grave, É., Bojanowski, P., Puhrsch, C. and Joulin, A. (2018). Advances in pre-training distributed word representations. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pp. 3111–3119.Google Scholar
Miller, G.A. (1995). WordNet: a lexical database for English. Communications of the ACM 38, 39–41.CrossRefGoogle Scholar
Mirza, P. (2016). Extracting temporal and causal relations between events. arXiv preprint arXiv:1604.08120.Google Scholar
Mirza, P., Sprugnoli, R., Tonelli, S. and Speranza, M. (2014). Annotating causality in the tempeval-3 corpus. In Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL), pp. 10–19.CrossRefGoogle Scholar
Mirza, P. and Tonelli, S. (2014). An analysis of causality between events and its relation to temporal information. In COLING, pp. 2097–2106.Google Scholar
Mirza, P. and Tonelli, S. (20160. Catena: causal and temporal relation extraction from natural language texts. In The 26th International Conference on Computational Linguistics. ACL, pp. 64–75.Google Scholar
Mostafazadeh, N., Grealish, A., Chambers, N., Allen, J. and Vanderwende, L. (2016). Caters: causal and temporal relation scheme for semantic annotation of event structures. In Proceedings of the 4th Workshop on Events: Definition, Detection, Coreference, and Representation, pp. 51–61.CrossRefGoogle Scholar
Mou, L., Meng, Z., Yan, R., Li, G., Xu, Y., Zhang, L. and Jin, Z. (2016). How transferable are neural networks in NLP applications? In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 479–489.Google Scholar
Mulkar-Mehta, R., Gordon, A.S., Hovy, E. and Hobbs, J.R. (2011a). Causal markers across domains and genres of discourse. In The 6th International Conference on Knowledge Capture, Banff, Alberta, Canada.CrossRefGoogle Scholar
Mulkar-Mehta, R., Welty, C., Hobbs, J.R. and Hovy, E. (2011b). Using granularity concepts for discovering causal relations. In Proceedings of the FLAIRS Conference.Google Scholar
Mulkar-Mehta, R., Welty, C.A., Hobbs, J.R. and Hovy, E.H. (2011c). Using part-of relations for discovering causality. In FLAIRS Conference.Google Scholar
Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.Google Scholar
Neeleman, A., and Van de Koot, H. et al. (2012). The linguistic expression of causation. In The Theta System: Argument Structure at the Interface, vol. 20.Google Scholar
Ning, Q., Feng, Z., Wu, H. and Roth, D. (2018). Joint reasoning for temporal and causal relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2278–2288.CrossRefGoogle Scholar
Nwaike, K. and Jiao, L. (2020). Counterfactual detection meets transfer learning. arXiv preprint arXiv:2005.13125.Google Scholar
O’Gorman, T., Wright-Bettner, K. and Palmer, M. (2016). Richer event description: integrating event coreference with temporal, causal and bridging annotation. In Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016), pp. 47–56.CrossRefGoogle Scholar
Oh, J.-H., Torisawa, K., Hashimoto, C., Sano, M., De Saeger, S. and Ohtake, K. (2013). Why-question answering using intra-and inter-sentential causal relations. In ACL (1), pp. 1733–1743.Google Scholar
Ojha, A.A., Garg, R., Gupta, S. and Modi, A. (2020). Iitk-rsa at semeval-2020 task 5: detecting counterfactuals. arXiv e-prints, pp. arXiv–2007.CrossRefGoogle Scholar
Onyshkevych, B. (1993). Template design for information extraction. In Proceedings of the 5th Conference on Message Understanding. Association for Computational Linguistics, pp. 19–23.CrossRefGoogle Scholar
Ovchinnikova, E., Montazeri, N., Alexandrov, T., Hobbs, J.R., McCord, M.C. and Mulkar-Mehta, R. (2014). Abductive reasoning with a large knowledge base for discourse processing. In Computing Meaning. Springer, pp. 107–127.CrossRefGoogle Scholar
Ovchinnikova, E., Vieu, L., Oltramari, A., Borgo, S. and Alexandrov, T. (2010). Data-driven and ontological analysis of framenet for natural language reasoning. In LREC.Google Scholar
Page, L., Brin, S., Motwani, R. and Winograd, T. (1999). The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab.Google Scholar
Palmer, M., Bonial, C. and Hwang, J.D. (2017). Verbnet: capturing english verb behavior, meaning and usage. In The Oxford Handbook of Cognitive Science, pp. 315–336.Google Scholar
Papanikolaou, Y., Roberts, I. and Pierleoni, A. (2019). Deep bidirectional transformers for relation extraction without supervision. In EMNLP-IJCNLP 2019, vol. 67.CrossRefGoogle Scholar
Pearl, J. (2012). The do-calculus revisited. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, pp. 3–11.Google Scholar
Pearl, J. and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.Google Scholar
Pechsiri, C. and Kawtrakul, A. (2007). Mining causality from texts for question answering system. IEICE Transactions on Information and Systems 90(10), 1523–1533.CrossRefGoogle Scholar
Pennington, J., Socher, R. and Manning, C.D. (2014). Glove: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543.CrossRefGoogle Scholar
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K. and Zettlemoyer, L. (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), New Orleans, Louisiana. Association for Computational Linguistics, pp. 2227–2237.CrossRefGoogle Scholar
Pichotta, K. and Mooney, R. (2016). Using sentence-level LSTM language models for script inference. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 279–289.CrossRefGoogle Scholar
Ponti, E.M. and Korhonen, A. (2017). Event-related features in feedforward neural networks contribute to identifying causal relations in discourse. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pp. 25–30.CrossRefGoogle Scholar
Preethi, P.G., Uma, V. and Ajit, K. (2015). Temporal sentiment analysis and causal rules extraction from tweets for event prediction. Procedia Computer Science, 48, 84–89.CrossRefGoogle Scholar
Psillos, S. (2007). Causal explanation and manipulation. In Rethinking Explanation. Springer, pp. 93–107.CrossRefGoogle Scholar
Puente, C., Garrido, E. and Olivas, J.A. (2013a). Answering questions by means of causal sentences. In International Conference on Flexible Query Answering Systems. Springer, pp. 91–99.CrossRefGoogle Scholar
Puente, C., Olivas, J. and Prado, I. (2014). Summarizing information by means of causal sentences. In Proceedings on the International Conference on Artificial Intelligence (ICAI), vol. 1. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).Google Scholar
Puente, C., Olivas, J.A., Garrido, E. and Seisdedos, R. (2013b). Creating a natural language summary from a compressed causal graph. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint. IEEE, pp. 513–518.CrossRefGoogle Scholar
Puent, e, C., Sobrino, A., Olivas, J. and Garrido, E. (2016). Summarizing information by means of causal sentences through causal graphs. Journal of Applied Logic 24, 3–14.CrossRefGoogle Scholar
Puente, C., Villa-Monte, A., Lanzarini, L., Sobrino, A. and Olivas, J.A. (2017). Evaluation of causal sentences in automated summaries. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE, pp. 1–6.CrossRefGoogle Scholar
Qiu, J., Xu, L., Zhai, J. and Luo, L. (2017). Extracting causal relations from emergency cases based on conditional random fields. Procedia Computer Science 112, 1623–1632.CrossRefGoogle Scholar
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I. (2019). Language models are unsupervised multitask learners. Available at https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (accessed October 2020).Google Scholar
Radinsky, K., Davidovich, S. and Markovitch, S. (2012). Learning causality for news events prediction. In Proceedings of the 21st International Conference on World Wide Web. ACM, pp. 909–918.CrossRefGoogle Scholar
Radinsky, K. and Horvitz, E. (2013). Mining the web to predict future events. In Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, WSDM’13. ACM, pp. 255–264.CrossRefGoogle Scholar
Rashmi, P., Nihkil, D., Alan, L., Eleni, M., Robaldo, L., Aravind, J., Bonnie, W.  et al. (2008). The penn discourse treebank 2.0. In Lexical Resources and Evaluation Conference.Google Scholar
Riaz, M. (2010). An Unsupervised Approach to Identifying Causal Relations from Relevant Scenarios. Master’s Thesis, University of Illinois.Google Scholar
Riaz, M. and Girju, R. (2010). Another look at causality: discovering scenario-specific contingency relationships with no supervision. In 2010 IEEE Fourth International Conference on Semantic Computing (ICSC). IEEE, pp. 361–368.CrossRefGoogle Scholar
Riaz, M. and Girju, R. (2014). Recognizing causality in verb-noun pairs via noun and verb semantics. In Proceedings of the Workshop on Computational Approaches to Causality in Language EACL 2014. The Association for Computer Linguistics.CrossRefGoogle Scholar
Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S. and Sontag, D. (2017). Learning a health knowledge graph from electronic medical records. Scientific Reports 7(1), 1–11.CrossRefGoogle ScholarPubMed
Russell, S.J., Norvig, P. and Norvig, P. (2003). Artificial Intelligence: Prentice Hall Series in Artificial Intelligence. Upper Saddle River, NJ: Pearson Education.Google Scholar
Sadek, J. (2013). Automatic detection of arabic causal relations. In Metais, E., Meziane, F., Saraee, M., Sugumaran, V. and Vadera, S. (eds), Natural Language Processing and Information Systems, Lecture Notes in Computer Science, vol. 7934. Berlin, Heidelberg: Springer, pp. 400–403.CrossRefGoogle Scholar
Sakai, H. and Masuyama, S. (2007). Extraction of cause information from newspaper articles concerning business performance. In Artificial Intelligence and Innovations 2007: from Theory to Applications, pp. 205–212.CrossRefGoogle Scholar
Sakaji, H., Sakai, H. and Masuyama, S. (2008a). Automatic Extraction of Basis Expressions That Indicate Economic Trends. Berlin, Heidelberg: Springer, pp. 977–984.Google Scholar
Sakaji, H., Sekine, S. and Masuyama, S. (2008b). Extracting causal knowledge using clue phrases and syntactic patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, LNAI, vol. 5345, pp. 111–122. cited By (since 1996)1.CrossRefGoogle Scholar
Sanchez-Graillet, O. and Poesio, M. (2004). Acquiring Bayesian networks from text. In LREC.Google Scholar
Sastre, J., Zaman, F., Duggan, N., McDonagh, C. and Walsh, P. (2020). A deep learning knowledge graph approach to drug labelling. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp. 2513–2521.CrossRefGoogle Scholar
Schuler, K.K. (2005). VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon. PhD Thesis, University of Pennsylvania, Philadelphia, PA, USA. AAI3179808.Google Scholar
Shapiro, S.C. (1992). Semantic networks. In Encyclopedia of Artificial Intelligence, 2nd Edn. New York, NY, USA: John Wiley & Sons, Inc.Google Scholar
Sharp, R., Surdeanu, M., Jansen, P., Clark, P. and Hammond, M. (2016). Creating causal embeddings for question answering with minimal supervision. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas. Association for Computational Linguistics, pp. 138–148.CrossRefGoogle Scholar
Sizov, G. and Öztürk, P. (2013). Automatic extraction of reasoning chains from textual reports. In Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing, pp. 61–69.Google Scholar
Sogaard, A. (2013). Semi-Supervised Learning and Domain Adaptation in Natural Language Processing, 1st Edn. Morgan & Claypool Publishers.CrossRefGoogle Scholar
Son, Y., Bayas, N. and Schwartz, H.A. (2018). Causal explanation analysis on social media. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3350–3359.CrossRefGoogle Scholar
Son, Y., Buffone, A., Raso, J., Larche, A., Janocko, A., Zembroski, K., Schwartz, H.A. and Ungar, L. (2017). Recognizing counterfactual thinking in social media texts. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 654–658.CrossRefGoogle Scholar
Tirunagari, S., Hanninen, M., Stanhlberg, K. and Kujala, P. (2012). Mining causal relations and concepts in maritime accidents investigation reports. International Journal of Innovative Research and Development 1(10), 548–566.Google Scholar
UzZaman, N., Llorens, H., Derczynski, L., Allen, J., Verhagen, M. and Pustejovsky, J. (2013). SemEval-2013 task 1: TempEval-3: evaluating time expressions, events, and temporal relations. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, Georgia, USA. Association for Computational Linguistics, pp. 1–9.Google Scholar
VanVactor, J.D. (2010). Health care logistics response in a disaster. Journal of Homeland Security and Emergency Management 7(1), 1–17.CrossRefGoogle Scholar
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, pp. 5998–6008.Google Scholar
Vendler, Z. (1967). Causal relations. The Journal of Philosophy 64(21), 704–713.CrossRefGoogle Scholar
Weber, N., Rudinger, R. and Van Durme, B. (2020). Causal inference of script knowledge. arXiv, arXiv–2004.CrossRefGoogle Scholar
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2019). Transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771.Google Scholar
Woodward, J. (2005). Making Things Happen: A Theory of Causal Explanation. Oxford University Press.Google Scholar
Wu, J.-L., Yu, L.-C. and Chang, P.-C. (2012). Detecting causality from online psychiatric texts using inter-sentential language patterns. BMC Medical Informatics and Decision Making 12(1), 1–10.CrossRefGoogle ScholarPubMed
Xu, R., Hu, J., Lu, Q., Wu, D. and Gui, L. (2017). An ensemble approach for emotion cause detection with event extraction and multi-kernel svms. Tsinghua Science and Technology 22(6), 646–659.CrossRefGoogle Scholar
Yang, B., Wu, J. and Hattori, G. (2020a). A transfer learning method of data collection for dialogue response generation concerning causal relation. In Proceedings of the Conference of the Japanese Society for Artificial Intelligence. Springer.CrossRefGoogle Scholar
Yang, X., Obadinma, S., Zhao, H., Zhang, Q., Matwin, S. and Zhu, X. (2020b). Semeval-2020 task 5: counterfactual recognition. arXiv preprint arXiv:2008.00563.CrossRefGoogle Scholar
Yang, Y., Wei, Z., Chen, Q. and Wu, L. (2019a). Using external knowledge for financial event prediction based on graph neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2161–2164.CrossRefGoogle Scholar
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., and Le, Q.V. (2019b). Xlnet: generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems, pp. 5753–5763.Google Scholar
Yu, B., Li, Y. and Wang, J. (2019). Detecting causal language use in science findings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4656–4666.CrossRefGoogle Scholar
Yu, B., Wang, J., Guo, L. and Li, Y. (2020). Measuring correlation-to-causation exaggeration in press releases. In Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain (Online). International Committee on Computational Linguistics, pp. 4860–4872.CrossRefGoogle Scholar
Yu, H. (2020a). Health causal probability knowledge graph: another intelligent health knowledge discovery approach. In 2020 7th International Conference on Bioinformatics Research and Applications, pp. 49–58.CrossRefGoogle Scholar
Yu, H.Q. (2020b). Dynamic causality knowledge graph generation for supporting the chatbot healthcare system. In Proceedings of the Future Technologies Conference. Springer, pp. 30–45.CrossRefGoogle Scholar
Zhang, Y., Jatowt, A. and Tanaka, K. (2016a). Causal relationship detection in archival collections of product reviews for understanding technology evolution. ACM Transactions on Information Systems (TOIS) 35(1), 3.CrossRefGoogle Scholar
Zhang, Y., Jatowt, A. and Tanaka, K. (2016b). Detecting evolution of concepts based on cause-effect relationships in online reviews. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 649–660.CrossRefGoogle Scholar
Zhao, S., Wang, Q., Massung, S., Qin, B., Liu, T., Wang, B. and Zhai, C. (2017). Constructing and embedding abstract event causality networks from text snippets. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, pp. 335–344.CrossRefGoogle Scholar
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H. and Xu, B. (2016). Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 207–212.CrossRefGoogle Scholar
Zuo, X., Chen, Y., Liu, K. and Zhao, J. (2020a). Knowdis: knowledge enhanced data augmentation for event causality detection via distant supervision. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 1544–1550.CrossRefGoogle Scholar
Zuo, X., Chen, Y., Liu, K. and Zhao, J. (2020b). Towards causal explanation detection with pyramid salient-aware network. In China National Conference on Chinese Computational Linguistics. Springer, pp. 113–128.CrossRefGoogle Scholar