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
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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
Extracting Causal Knowledge Using Clue Phrases and Syntactic Patterns
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Using a Bigram Event Model to Predict Causal Potential
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Journal of Biomedical Informatics
Automatic discovery of manner relations and its applications
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Emotion cause detection with linguistic constructions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Unsupervised learning of semantic relation composition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A model for composing semantic relations
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Node-first causal network extraction for trend analysis based on web mining
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Learning high-level planning from text
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Composition of semantic relations: Theoretical framework and case study
ACM Transactions on Speech and Language Processing (TSLP)
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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.