Summary of the paper

Title JFCKB: Japanese Feature Change Knowledge Base
Authors Tetsuaki Nakamura and Daisuke Kawahara
Abstract 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.
Topics Crowdsourcing, Emotion Recognition/Generation, Lexicon, Lexical Database
Full paper JFCKB: Japanese Feature Change Knowledge Base
Bibtex @InProceedings{NAKAMURA18.244,
  author = {Tetsuaki Nakamura and Daisuke Kawahara},
  title = "{JFCKB: Japanese Feature Change Knowledge Base}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
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