| Paper Abstract and Keywords |
| Presentation |
2012-08-31 15:10
Feature Words that Describe Problem Sentence in Scientific Article Toshihiko Sakai, Sachio Hirokawa (Kyushu Univ.) NLC2012-23 |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
The literature survey on related work requires a lot of efforts in reading all articles that are retrieved by queries. We have to understand the contents from several view points, such as the problem and the method that the articles describe. Search from these viewpoints will improve the efficiency of survey, if particular segments of articles were extracted, indexed and can be used as auxiliary query.
This paper focuses on the sentence that describes the problem in an abstract and the feature sets that describe such problem sentences. Discernment performance are evaluated by 10-fold cross-validation for six candidates of feature word sets. It turned out that the set of all words gains the best performance if 90% of the data are used as training data. However, the set of a small number of words with positive scores outperforms other feature sets, if the training data is only 10%. In such a realistic situation, the feature words are effective in improving discernment. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Feature Words / SVM / Cross validation / Scientific Article / / / / |
| Reference Info. |
IEICE Tech. Rep., vol. 112, no. 196, NLC2012-23, pp. 73-78, Aug. 2012. |
| Paper # |
NLC2012-23 |
| Date of Issue |
2012-08-23 (NLC) |
| 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) |
| Download PDF |
NLC2012-23 |
| Conference Information |
| Committee |
NLC |
| Conference Date |
2012-08-30 - 2012-08-31 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Fuji Xerox |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
The Second Symposium on Text Mining |
| Paper Information |
| Registration To |
NLC |
| Conference Code |
2012-08-NLC |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Feature Words that Describe Problem Sentence in Scientific Article |
| Sub Title (in English) |
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| Keyword(1) |
Feature Words |
| Keyword(2) |
SVM |
| Keyword(3) |
Cross validation |
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Scientific Article |
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| 1st Author's Name |
Toshihiko Sakai |
| 1st Author's Affiliation |
Kyushu University (Kyushu Univ.) |
| 2nd Author's Name |
Sachio Hirokawa |
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Kyushu University (Kyushu Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2012-08-31 15:10:00 |
| Presentation Time |
30 minutes |
| Registration for |
NLC |
| Paper # |
NLC2012-23 |
| Volume (vol) |
vol.112 |
| Number (no) |
no.196 |
| Page |
pp.73-78 |
| #Pages |
6 |
| Date of Issue |
2012-08-23 (NLC) |