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Title: Leveraging eventive information for better metaphor detection and classification
Authors: Chen, IH 
Long, Y 
Lu, Q 
Huang, CR 
Issue Date: 2017
Source: In Proceedings of the 21st Conference on Computational Natural Language Learning, CoNLL 2017, Vancouver, Canada, 3-4 August 2017, p. 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.
Publisher: Association for Computational Linguistics (ACL)
ISBN: 9.78E+12
DOI: 10.18653/v1/k17-1006
Rights: © 2017 Association for Computational Linguistics
ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (
The following publication Chen, I. -., Long, Y., Lu, Q., & Huang, C. -. (2017). Leveraging eventive information for better metaphor detection and classification. Paper presented at the CoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings, 36-46 is available at
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