Abstract
This chapter presents an approach for the discovery of causal relations from open domain text in English. The approach is hybrid, indeed it joins rules based and machine learning methodologies in order to combine the advantages of both. The approach first identifies a set of plausible cause-effect pairs through a set of logical rules based on dependencies between words, then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate our model. The results demonstrate the ability of the rules for the relation extraction and the improvements made by the filtering process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
One of the most prominent examples is http://www.recordedfuture.com/.
References
Atzeni, P., Basili, R., Hansen, D.H., Missier, P., Paggio, P., Pazienza, M.T., Zanzotto, F.M.: Ontology-based question answering in a federation of university sites: the moses case study. In: NLDB. 413–420 (2004)
Girju, R., Moldovan, D.: Mining answers for causation questions. In: AAAI symposium on (2002)
Mele, F., Sorgente, A.: OntoTimeFL—a formalism for temporal annotation and reasoning for natural language text. In: Lai, C., Semeraro, G., Vargiu, E. (eds.) New Challenges in Distributed Information Filtering and Retrieval. Studies in Computational Intelligence, vol. 439, pp. 151–170. Springer, Berlin (2013)
Mele, F., Sorgente, A., Vettigli, G.: Designing and building multimedia cultural stories using concepts of film theories and logic programming. In: AAAI Fall Symposium: Cognitive and Metacognitive Educational Systems (2010)
Ittoo, A., Bouma, G.: Extracting explicit and implicit causal relations from sparse, domain-specific texts. In: Proceedings of the 16th International Conference on Natural Language Processing and Information Systems. NLDB’11, pp. 52–63. Springer, Berlin (2011)
Blanco, E., Castell, N., Moldovan, D.I.: Causal relation extraction. In: LREC (2008)
Girju, R., Nakov, P., Nastase, V., Szpakowicz, S., Turney, P., Yuret, D.: Classification of semantic relations between nominals. Lang. Resour. Eval. 43(2), 105–121 (2009)
Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Saghdha, D., Romano, L., Szpakowicz, S.: Semeval-2010 task 8: multi-way classification of semantic relations between pairs of nominals
Rink, B., Harabagiu, S.: Utd: Classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th International Workshop on Semantic Evaluation. SemEval ’10, Stroudsburg, PA, USA, Association for Computational Linguistics (2010) 256–259
Radinsky, K., Davidovich, S.: Learning to predict from textual data. J. Artif. Int. Res. 45(1), 641–684 (2012)
Khoo, C.S.G., Chan, S., Niu, Y.: Extracting causal knowledge from a medical database using graphical patterns. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. ACL ’00, Stroudsburg, PA, USA, pp. 336–343. Association for Computational Linguistics (2000)
Kruijff, G.J.: Dependency grammar. In: Brown, K. (ed.) Encyclopedia of Language and Linguistics (Second Edition), 2nd edn, pp. 444–450. Elsevier, Oxford (2006)
Covington, M.A.: A fundamental algorithm for dependency parsing. In: Proceedings of the 39th Annual ACM Southeast Conference, pp. 95–102 (2001)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning. ICML ’97, San Francisco, CA, USA, pp. 412–420. Morgan Kaufmann Publishers Inc. (1997)
Marneffe, M.C.D., Manning, C.D.: Stanford Typed Dependencies Manual (2008)
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
De Marneffe, M.C., Maccartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: LREC 2006 (2006)
Sagonas, K., Swift, T., Warren, D.S.: Xsb as an efficient deductive database engine. In: Proceedings of the ACM SIGMOD International Conference on the Management of Data, pp. 442–453. ACM Press (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Sorgente, A., Vettigli, G., Mele, F. (2018). A Hybrid Approach for the Automatic Extraction of Causal Relations from Text. In: Lai, C., Giuliani, A., Semeraro, G. (eds) Emerging Ideas on Information Filtering and Retrieval. Studies in Computational Intelligence, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-319-68392-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-68392-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68390-4
Online ISBN: 978-3-319-68392-8
eBook Packages: EngineeringEngineering (R0)