Presentation 2022-03-07
Simile identification based on machine learning using pseudo data acquisition
Jintaro Jimi, Kazutaka Shimada,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Simile is a kind of figurative language. It expresses the target of the figurative language by using comparators such as ``like''. For understanding a sentence, it is important to distinguish whether the sentence is a simile or a literal. In this paper, we propose a pseudo dataset acquisition method for simile identification. We first constructed a dataset of simile and literal sentences using machine translation. Next, we define the simile identification task as a binary classification problem. We apply some machine learning approaches to the task. We show the validity of the pseudo dataset and the models in this task.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) simile identification / pseudo data / automatic training data acquisition / figurative language
Paper # NLC2021-36
Date of Issue 2022-02-28 (NLC)

Conference Information
Committee NLC
Conference Date 2022/3/7(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Information Processing of Tourism, etc.
Chair Kazutaka Shimada(Kyushu Inst. of Tech.)
Vice Chair Mitsuo Yoshida(Toyohashi Univ. of Tech.) / Takeshi Kobayakawa(NHK)
Secretary Mitsuo Yoshida(Univ. of Tokyo) / Takeshi Kobayakawa(Hiroshima Univ. of Economics)
Assistant Kanjin Takahashi(Sansan) / Ko Mitsuda(NTT)

Paper Information
Registration To Technical Committee on Natural Language Understanding and Models of Communication
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Simile identification based on machine learning using pseudo data acquisition
Sub Title (in English)
Keyword(1) simile identification
Keyword(2) pseudo data
Keyword(3) automatic training data acquisition
Keyword(4) figurative language
1st Author's Name Jintaro Jimi
1st Author's Affiliation Kyushu Institute of Technology(Kyutech)
2nd Author's Name Kazutaka Shimada
2nd Author's Affiliation Kyushu Institute of Technology(Kyutech)
Date 2022-03-07
Paper # NLC2021-36
Volume (vol) vol.121
Number (no) NLC-415
Page pp.pp.48-53(NLC),
#Pages 6
Date of Issue 2022-02-28 (NLC)