Presentation 2017-09-18
Comparative consideration of automatic sentence generation method
Ota Hiromitsu,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) The era of big data has arrived, several years have passed since it was established. Especially the development of deep learning in recent years is remarkable. However, to take advantage of it, the cost aspect of how much text is needed is important in practice. In this paper, we compare and consider efficient method of text quantity. Mainly used methods are as follows. 1) Markov chain, 2) automatic summarization, 3) is scheduled to sentence generated by deep learning (RNN / LSTM).
Keyword(in Japanese) (See Japanese page)
Keyword(in English) deep learning / big data / natural language processing / Markov chain / automatic summarization / RNN / LSTM / quantity of texts
Paper # DE2017-13
Date of Issue 2017-09-11 (DE)

Conference Information
Conference Date 2017/9/18(3days)
Place (in Japanese) (See Japanese page)
Place (in English) Ochanomizu University
Topics (in Japanese) (See Japanese page)
Topics (in English) Big Data Management, Information Retrieval, Knowledge Discovery, etc.
Chair Akiyo Nadamoto(Konan Univ.)
Vice Chair Koji Eguchi(Kobe Univ.) / Shingo Otsuka(Kanagawa Inst. of Tech.)
Secretary Koji Eguchi(Kogakuin Univ.) / Shingo Otsuka(Univ. of Marketing and Distrbution Science)
Assistant Kazuo Goda(Univ. of Tokyo) / Yuroaki Shiokawa(Tsukuba Univ.)

Paper Information
Registration To Technical Committee on Data Engineering / Special Interest Group on Database System / Special Interest Group on Information Fundamentals and Access Technologies
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Comparative consideration of automatic sentence generation method
Sub Title (in English) Issues and measures of connection among sentences in sentence generation
Keyword(1) deep learning
Keyword(2) big data
Keyword(3) natural language processing
Keyword(4) Markov chain
Keyword(5) automatic summarization
Keyword(6) RNN
Keyword(7) LSTM
Keyword(8) quantity of texts
1st Author's Name Ota Hiromitsu
1st Author's Affiliation University of Air(Univ. of Air)
Date 2017-09-18
Paper # DE2017-13
Volume (vol) vol.117
Number (no) DE-212
Page pp.pp.1-6(DE),
#Pages 6
Date of Issue 2017-09-11 (DE)