Presentation 2017-09-08
Extraction of causal information from summaries of financial statements
Hiroyuki Sakai, Kazuki Matsushita,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this research, we propose a method to extract sentences including causal information concerning business performance (e.g. "Orders of semiconductor manufacturing equipments were good") from summary of financial statements. A previous research to extract sentences including causal information concerning business performance from summary of financial statements exists. In contrast, our method automatically generates training data by more accurately narrowing down the sentences extracted by the previous method. Using them, our method extracts more sentences including causal information concerning business performance than the previous method by deep learning.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Causal information / Summaries of financial statements / Text mining / Natural language processing / Information extraction
Paper # NLC2017-33
Date of Issue 2017-08-31 (NLC)

Conference Information
Committee NLC
Conference Date 2017/9/7(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Seikei University
Topics (in Japanese) (See Japanese page)
Topics (in English) The Eleventh Text Analytics Symposium
Chair Hiroshi Kanayama(IBM)
Vice Chair Takeshi Sakaki(Hottolink) / Kazutaka Shimada(Kyushu Inst. of Tech.)
Secretary Takeshi Sakaki(Ryukoku Univ.) / Kazutaka Shimada(NTT)
Assistant Mitsuo Yoshida(Toyohashi Univ. of Tech.) / Takeshi Kobayakawa(NICT)

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) Extraction of causal information from summaries of financial statements
Sub Title (in English)
Keyword(1) Causal information
Keyword(2) Summaries of financial statements
Keyword(3) Text mining
Keyword(4) Natural language processing
Keyword(5) Information extraction
1st Author's Name Hiroyuki Sakai
1st Author's Affiliation SEIKEI University(SEIKEI Univ.)
2nd Author's Name Kazuki Matsushita
2nd Author's Affiliation SEIKEI University(SEIKEI Univ.)
Date 2017-09-08
Paper # NLC2017-33
Volume (vol) vol.117
Number (no) NLC-207
Page pp.pp.87-91(NLC),
#Pages 5
Date of Issue 2017-08-31 (NLC)