A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Knowledge-based acquisition of causal relationships in text
Knowledge Acquisition
WordNet: a lexical database for English
Communications of the ACM
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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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
Causal relation extraction using cue phrase and lexical pair probabilities
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
ChronoSeeker: Future Opinion Extraction
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Composition of semantic relations: model and applications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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This work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair and concept pair that could be part of causal noun phrase pairs. We also use the cue phrase probability that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. With this probabilistic model, we increase both precision and recall. Our causality extraction shows an F-score of 77.37%, which is an improvement of 21.14 percentage points over the baseline model. The long distance causal relation is extracted with the binary tree-styled cue phrase. We propose an incremental cue phrase learning method based on the cue phrase confidence score that was measured after each causal classifier learning step. A better recall of 15.37 percentage points is acquired after the cue phrase learning.