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
Cause Information Extraction from Financial Articles Concerning Business Performance
IEICE - Transactions on Information and Systems
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
Information Processing and Management: an International Journal
Automatic extraction of basis expressions that indicate economic trends
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An incremental method for causal network construction
WAIM'10 Proceedings of the 11th international conference on Web-age information management
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This paper proposes a method to extract causal knowledge (cause and effect relations) using clue phrases and syntactic patterns from Japanese newspaper articles concerning economic trends. For example, a sentence fragment "World economy recession due to the subprime loan crisis ..." contains causal knowledge in which "World economy recession" is an effect phrase and "the subprime loan crisis" is its cause phrase. These relations are found by clue phrases, such as "******(tame : because)" and "*********(niyori : due to)". We, first, investigated newspaper corpus by annotating causal knowledge and clue phrases. We found that some specific syntactic patterns are useful to improve accuracy to extract causal knowledge. Finally, we developed our system using the clue phrases and the syntactic patterns and showed the evaluation results on a large corpus.