@InProceedings{chen-EtAl:2017:CoNLL1,
  author    = {Chen, I-Hsuan  and  Long, Yunfei  and  Lu, Qin  and  Huang, Chu-Ren},
  title     = {Leveraging Eventive Information for Better Metaphor Detection and Classification},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {36--46},
  abstract  = {Metaphor detection has been both challenging and rewarding in natural language
	processing applications. This study offers a new approach based on eventive
	information in detecting metaphors by leveraging the Chinese writing system,
	which is a culturally bound ontological system organized according to the basic
	concepts represented by radicals. As such, the information represented is
	available in all Chinese text without pre-processing. Since metaphor detection
	is another culturally based conceptual representation, we hypothesize that
	sub-textual information can facilitate the identification and classification of
	the types of metaphoric events denoted in Chinese text. We propose a set of
	syntactic conditions crucial to event structures to improve the model based on
	the classification of radical groups. With the proposed syntactic conditions,
	the model achieves a performance of 0.8859 in terms of F-scores, making 1.7% of
	improvement than the same classifier with only Bag-of-word features. Results
	show that eventive information can improve the effectiveness of metaphor
	detection. Event information is rooted in every language, and thus this
	approach has a high potential to be applied to metaphor detection in other
	languages.},
  url       = {http://aclweb.org/anthology/K17-1006}
}