Using cause-effect relations in text to improve information retrieval precision
Information Processing and Management: an International Journal
Part of speech tagging using a network of linear separators
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Machine learning of temporal relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning semantic links from a corpus of parallel temporal and causal relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Information Processing and Management: an International Journal
Minimally supervised event causality identification
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning high-level planning from text
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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This paper addresses the problem of causal knowledge discovery. Using online screenplays, we generate a corpus of temporally ordered events. We then introduce a measure we call causal potential which is easily calculated with statistics gathered over the corpus and show that this measure is highly correlated with an event pair's tendency of encoding a causal relation. We suggest that causal potential can be used in systems whose task is to determine the existence of causality between temporally adjacent events, when critical context is either missing or unreliable. Moreover, we argue that our model should therefore be used as a baseline for standard supervised models which take into account contextual information.