The nature of statistical learning theory
The nature of statistical learning theory
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Machine Learning
COATIS, an NLP System to Locate Expressions of Actions Connected by Causality Links
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Statistical Models for Co-occurrence Data
Statistical Models for Co-occurrence Data
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th 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
Investigating the characteristics of causal relations in Japanese text
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
Causal relation extraction using cue phrase and lexical pair probabilities
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Latent variable discovery in classification models
Artificial Intelligence in Medicine
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We describe statistical models for detecting causality between two events. Our models are kinds of latent variable models, actually expanded versions of the existing statistical co-occurrence models. The (statistical) dependency information between two events needs to be incorporated into causal models. We handle this information via latent variables in our models. Through experiments, we achieved .678 F-measure value for the evaluation data.