Abstract
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 “ため(tame: because)” and “により(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.
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Sakaji, H., Sekine, S., Masuyama, S. (2008). Extracting Causal Knowledge Using Clue Phrases and Syntactic Patterns. In: Yamaguchi, T. (eds) Practical Aspects of Knowledge Management. PAKM 2008. Lecture Notes in Computer Science(), vol 5345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89447-6_12
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DOI: https://doi.org/10.1007/978-3-540-89447-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89446-9
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