Abstract
This work aims to extract causal relations that exist between two events expressed by noun phrases or sentences. The previous works for the causality made use of causal patterns such as causal verbs. We concentrate on the information obtained from other causal event pairs. If two event pairs share some lexical pairs and one of them is revealed to be causally related, the causal probability of another event pair tends to increase. We introduce the lexical pair probability and the cue phrase probability. These probabilities are learned from raw corpus in unsupervised manner. With these probabilities and the Naive Bayes classifier, we try to resolve the causal relation extraction problem. Our inter-NP causal relation extraction shows the precision of 81.29%, that is 7.05% improvement over the baseline model. The proposed models are also applied to inter-sentence causal relation extraction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, D.-S., Choi, K.-S.: Unsupervised learning of the dependency grammar using inside and outside probabilities, in Proceedings of the 12th Hangul and Korean Information Processing (2000) (in Korean)
Girju, R.: Automatic Detection of Causal Relation for Question Answering. In: Proceeding of Workshop in the 41st Annual Meeting of the Association for Computational Linguistics Conference (2003)
Girju, R., Moldovan, D.: Mining Answers for Causation Questions. In: Proceeding of AAAI Symposium on Mining Answers from Texts and Knowledge Bases (2002)
HealthChosun Medical Library, http://hpsearch.drline.net/dizzo/healthinfo/healthinfo.asp
Joins HealthCare Medical Encyclopedia, http://healthcare.joins.com/library
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 of the Association for Computational Linguistics (2000)
Khoo, C.S.G., Kornfit, J., Oddy, R.N., Myaeng, S.H.: Automatic Extraction of Cause-Effect Information from Newspaper Text without Knowledge-Based Inferencing. Literary and Linguistic Computing 13(4), 177–186 (1998)
Marcu, D., Echihabi, A.: An Unsupervised Approach to Recognizing Discourse Relations. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics Conference, Philadelphia, PA (2002)
Medical Subject Heading (2004), http://www.nlm.nih.gov/mesh
Miller, G.: WordNet: a Lexical Database. Communications of the ACM 38(11), 39–41 (1995)
Modovan, D.I., Pasca, M., Harabagiu, S.M., Surdeanu, M.: Performance Issues and Error Analysis in an Open-Domain Question Answering. ACM Transactions on Information Systems 21(2), 133–154 (2003)
Moldovan, D.I., Harabagiu, S.M., Girju, R., Morarescu, P., Lacatusu, F., Novischi, A., Badulescu, A., Bolohan, O.: LCC Tools for Question Answering. In: Proceedings of the 11th Text Retrieval Conference, NIST (2002)
Nigram, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39(2/3), 103–134 (2000)
Tapanainen, P., Jarvinen, T.: A non-projective dependency parser. In: Proceedings of the 5th Conference on Applied Natural Language Processing, Association for Computational Linguistics, pp. 64–71 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chang, DS., Choi, KS. (2005). Causal Relation Extraction Using Cue Phrase and Lexical Pair Probabilities. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-30211-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24475-2
Online ISBN: 978-3-540-30211-7
eBook Packages: Computer ScienceComputer Science (R0)