Paper Abstract and Keywords |
Presentation |
2014-11-17 17:00
[Poster Presentation]
Reconstructive Neural Net based on Context Free Grammar Takuo Hamaguchi, Masashi Shimbo, Yuji Matsumoto (NAIST) IBISML2014-48 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
There are two aspects about Neural Language Model,one aspect, `Encode', obtains a vector from a sequence and the other aspect, `Decode', obtains a sequence from a vector.Recurrent Neural Network (Recurrent NN) is a kind of `Decode' model which repeats prediction and transition from vector-represented state through time.However, a tree structure based model such as Context Free Grammar is more natural for natural languate tha a time-series structure based model.This paper proposes new tree structure `Decode' Model called Reconstrucitve Neural Network (Reconstructive NN).We conduct the following four experiemtns to show this model's ability:comparision of Recurrent NN and Reconstructive NN,relationship between the accuracy and the Distribution of word length included training data,relationship between the vector dimension and the words length,and the ability of Reconstructive NN comes from natural language. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep Learning / Distributed Representation / Recurrent Neural Network / Recursive Nerural Network / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 114, no. 306, IBISML2014-48, pp. 103-110, Nov. 2014. |
Paper # |
IBISML2014-48 |
Date of Issue |
2014-11-10 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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IBISML2014-48 |
Conference Information |
Committee |
IBISML |
Conference Date |
2014-11-17 - 2014-11-19 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Nagoya Univ. |
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(See Japanese page) |
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Paper Information |
Registration To |
IBISML |
Conference Code |
2014-11-IBISML |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Reconstructive Neural Net based on Context Free Grammar |
Sub Title (in English) |
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Keyword(1) |
Deep Learning |
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Distributed Representation |
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Recurrent Neural Network |
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Recursive Nerural Network |
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1st Author's Name |
Takuo Hamaguchi |
1st Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
2nd Author's Name |
Masashi Shimbo |
2nd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
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Yuji Matsumoto |
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Nara Institute of Science and Technology (NAIST) |
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Speaker |
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Date Time |
2014-11-17 17:00:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2014-48 |
Volume (vol) |
vol.114 |
Number (no) |
no.306 |
Page |
pp.103-110 |
#Pages |
8 |
Date of Issue |
2014-11-10 (IBISML) |
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