Artificial Intelligence - Special volume on natural language processing
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Communications of the ACM
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Natural language processing and knowledge representation: language for knowledge and knowledge for language
Plan Recognition in Natural Language Dialogue
Plan Recognition in Natural Language Dialogue
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Inside Computer Understanding: Five Programs Plus Miniatures
Inside Computer Understanding: Five Programs Plus Miniatures
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on 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
Open Mind Common Sense: Knowledge Acquisition from the General Public
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Character and Document Research in the Open Mind Initiative
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
TextNet – A text-based intelligent system
Natural Language Engineering
The rhetorical parsing of natural language texts
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Toward memory-based translation
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
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
Japanese dependency analysis using cascaded chunking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
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
Statement map: assisting information crediblity analysis by visualizing arguments
Proceedings of the 3rd workshop on Information credibility on the web
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
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
Mining term networks from text collections for crime investigation
Expert Systems with Applications: An International Journal
Granular Causality Applications: Using Part-of Relations for Discovering Causality
International Journal of Cognitive Informatics and Natural Intelligence
Granular Causality Applications: Using Part-of Relations for Discovering Causality
International Journal of Cognitive Informatics and Natural Intelligence
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In this paper, we deal with automatic knowledge acquisition from text, specifically the acquisition of causal relations. A causal relation is the relation existing between two events such that one event causes (or enables) the other event, such as “hard rain causes flooding” or “taking a train requires buying a ticket.” In previous work these relations have been classified into several types based on a variety of points of view. In this work, we consider four types of causal relations---cause, effect, precond(ition) and means---mainly based on agents' volitionality, as proposed in the research field of discourse understanding. The idea behind knowledge acquisition is to use resultative connective markers, such as “because,” “but,” and “if” as linguistic cues. However, there is no guarantee that a given connective marker always signals the same type of causal relation. Therefore, we need to create a computational model that is able to classify samples according to the causal relation. To examine how accurately we can automatically acquire causal knowledge, we attempted an experiment using Japanese newspaper articles, focusing on the resultative connective “tame.” By using machine-learning techniques, we achieved 80% recall with over 95% precision for the cause, precond, and means relations, and 30% recall with 90% precision for the effect relation. Furthermore, the classification results suggest that one can expect to acquire over 27,000 instances of causal relations from 1 year of Japanese newspaper articles.