TY - JOUR
T1 - What kinds and amounts of causal knowledge can be acquired from text by using connective markers as clues?
AU - Inui, Takashi
AU - Inui, Kentaro
AU - Matsumoto, Yuji
PY - 2003
Y1 - 2003
N2 - This paper reports the results of our ongoing research into the automatic acquisition of causal knowledge. We created a new typology for expressing the causal relations - cause, effect, precondition) and means -based mainly on the volitionality of the related events. From our experiments using the Japanese resultative connective "tame", 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. The results indicate that over 27,000 instances of causal relations can be acquired from one year of Japanese newspaper articles.
AB - This paper reports the results of our ongoing research into the automatic acquisition of causal knowledge. We created a new typology for expressing the causal relations - cause, effect, precondition) and means -based mainly on the volitionality of the related events. From our experiments using the Japanese resultative connective "tame", 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. The results indicate that over 27,000 instances of causal relations can be acquired from one year of Japanese newspaper articles.
UR - http://www.scopus.com/inward/record.url?scp=0242266100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0242266100&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-39644-4_16
DO - 10.1007/978-3-540-39644-4_16
M3 - Article
AN - SCOPUS:0242266100
SN - 0302-9743
VL - 2843
SP - 180
EP - 193
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
ER -