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| Presentation | 2022-09-13 15:30 Extraction of sentences representing company-specific competitive advantages from integrated reports Yuta Sugawara, Hiroyuki Sakai, Kengo Enami (Seikei), Kaito Takano, Kei Nakagawa (Nomura Asset Management)  NLC2022-7 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this research, we propose a method for extracting sentences representing company-specific competitive advantages from integrated reports by deep learning. For example, our method extracts sentences that include the company's strengths, specific management strategies, and business development such as “The automotive products business of the Sumitomo Science & Engineering Group handles anti-vibration rubber and automotive hoses, which boast the largest market share in the world, as well as interior and sound insulation products that contribute to ride comfort and safety.”. Form these sentences, it is possible to understand a company's unique strengths and the points in which the company is superior among companies in the same industry. Our method uses BERT as a classifier with fine tuning, so that even a small amount of training data can be classified with a reasonably high accuracy. In addition, our method restricts the classification results by the degree of similarity between sentences, and attained 0.713 precision. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | integrated reports / sentence identification / machine learning / clue phrases / information extraction / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 180, NLC2022-7, pp. 13-18, Sept. 2022. | |
| Paper # | NLC2022-7 | |
| Date of Issue | 2022-09-06 (NLC) | |
| ISSN | 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 | NLC2022-7 |
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| Conference Information | |
| Committee | NLC |
| Conference Date | 2022-09-13 - 2022-09-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Keio Univ. Yagami Campus. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The 19th Text Analytics Symposium |
| Paper Information | |
| Registration To | NLC |
| Conference Code | 2022-09-NLC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Extraction of sentences representing company-specific competitive advantages from integrated reports |
| Sub Title (in English) | |
| Keyword(1) | integrated reports |
| Keyword(2) | sentence identification |
| Keyword(3) | machine learning |
| Keyword(4) | clue phrases |
| Keyword(5) | information extraction |
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| 1st Author's Name | Yuta Sugawara |
| 1st Author's Affiliation | Seikei University (Seikei) |
| 2nd Author's Name | Hiroyuki Sakai |
| 2nd Author's Affiliation | Seikei University (Seikei) |
| 3rd Author's Name | Kengo Enami |
| 3rd Author's Affiliation | Seikei University (Seikei) |
| 4th Author's Name | Kaito Takano |
| 4th Author's Affiliation | Nomura Asset Management Co. (Nomura Asset Management) |
| 5th Author's Name | Kei Nakagawa |
| 5th Author's Affiliation | Nomura Asset Management Co. (Nomura Asset Management) |
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| Speaker | Author-1 |
| Date Time | 2022-09-13 15:30:00 |
| Presentation Time | 25 minutes |
| Registration for | NLC |
| Paper # | NLC2022-7 |
| Volume (vol) | vol.122 |
| Number (no) | no.180 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2022-09-06 (NLC) |
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