WordNet: a lexical database for English
Communications of the ACM
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Performance issues and error analysis in an open-domain question answering system
ACM Transactions on Information Systems (TOIS)
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Extracting causal knowledge from a medical database using graphical patterns
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Information Processing and Management: an International Journal
Mining explanation knowledge from textual data
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Mining causality knowledge from textual data
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
ACM Transactions on Asian Language Information Processing (TALIP)
Mining Causality from Texts for Question Answering System
IEICE - Transactions on Information and Systems
Latent Variable Models for Causal Knowledge Acquisition
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Know-why extraction from textual data for supporting what question
KRAQ '08 Coling 2008: Proceedings of the workshop on Knowledge and Reasoning for Answering Questions
Information Processing and Management: an International Journal
Explanation knowledge graph construction through causality extraction from texts
Journal of Computer Science and Technology
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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.