TY - GEN
T1 - JFCKB
T2 - 11th International Conference on Language Resources and Evaluation, LREC 2018
AU - Nakamura, Tetsuaki
AU - Kawahara, Daisuke
N1 - Funding Information:
This work was supported by JST PRESTO Grant Number JPMJPR1402, Japan.
Publisher Copyright:
© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Commonsense knowledge plays an essential role in our language activities. Although many projects have aimed to develop language resources for commonsense knowledge, there is little work focusing on connotational meanings. This is because constructing commonsense knowledge including connotational meanings is challenging. In this paper, we present a Japanese knowledge base where arguments in event sentences are associated with various feature changes caused by the events. For example, “my child” in “my wife hits my child” is associated with some feature changes, such as increase in pain, increase in anger, increase in disgust, and decrease in joy. We constructed this knowledge base through crowdsourcing tasks by gathering feature changes of arguments in event sentences. After the construction of the knowledge base, we conducted an experiment in anaphora resolution using the knowledge base. We regarded anaphora resolution as an antecedent candidate ranking task and used Ranking SVM as the solver. Experimental results demonstrated the usefulness of our feature change knowledge base.
AB - Commonsense knowledge plays an essential role in our language activities. Although many projects have aimed to develop language resources for commonsense knowledge, there is little work focusing on connotational meanings. This is because constructing commonsense knowledge including connotational meanings is challenging. In this paper, we present a Japanese knowledge base where arguments in event sentences are associated with various feature changes caused by the events. For example, “my child” in “my wife hits my child” is associated with some feature changes, such as increase in pain, increase in anger, increase in disgust, and decrease in joy. We constructed this knowledge base through crowdsourcing tasks by gathering feature changes of arguments in event sentences. After the construction of the knowledge base, we conducted an experiment in anaphora resolution using the knowledge base. We regarded anaphora resolution as an antecedent candidate ranking task and used Ranking SVM as the solver. Experimental results demonstrated the usefulness of our feature change knowledge base.
KW - Anaphora resolution
KW - Commonsense knowledge
KW - Crowdsourcing
KW - Emotion
KW - Knowledge base
UR - http://www.scopus.com/inward/record.url?scp=85059917246&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059917246&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85059917246
T3 - LREC 2018 - 11th International Conference on Language Resources and Evaluation
SP - 1398
EP - 1404
BT - LREC 2018 - 11th International Conference on Language Resources and Evaluation
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Piperidis, Stelios
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Hasida, Koiti
A2 - Mazo, Helene
A2 - Choukri, Khalid
A2 - Goggi, Sara
A2 - Mariani, Joseph
A2 - Moreno, Asuncion
A2 - Calzolari, Nicoletta
A2 - Odijk, Jan
A2 - Tokunaga, Takenobu
PB - European Language Resources Association (ELRA)
Y2 - 7 May 2018 through 12 May 2018
ER -