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| Paper Abstract and Keywords | ||
| Presentation | 2020-11-13 15:22 [Poster Presentation] Automation of Ontology Generation by Pre-trained Language Model Atusi Oba, Ayato Kuwana, Paik Incheon (UoA)  KBSE2020-22 SC2020-26 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | As an initial attempt of ontology generation with neural network, Recurrent Neural Network (RNN) based method is proposed recently. This method efficiently achieves the ontology generation task by combining sequential neural network and pretrained vector representation of words. However, updating the architecture is possible due to the rapid development of Natural Language Processing (NLP). In other hand, transfer learning of language models trained by huge unlabeled corpus such as BERT has brought a breakthrough in NLP. These methods allowed the language model to have a wide language knowledge and its processing ability by two-stage learning which divide training procedure into two stages, Pre-training and Fine-tuning. Inspired by these achievements, to apply transfer learning of language model, we propose novel workflow for ontology generation consists of two-stage learning. This paper also provides a quantitative comparison between the proposed method and existing methods. Empirical evidence showed that our best configuration update previous accuracy over 12.5% without sophisticated structure. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | ontology / neural network / pre-trained model / natural language processing / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 231, KBSE2020-22, pp. 40-40, Nov. 2020. | |
| Paper # | KBSE2020-22 | |
| Date of Issue | 2020-11-06 (KBSE, SC) | |
| 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) | |
| Notes on Review | This article is a technical report without peer review, and its polished version will be published elsewhere. | |
| Download PDF | KBSE2020-22 SC2020-26 |
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| Conference Information | |
| Committee | KBSE SC |
| Conference Date | 2020-11-13 - 2020-11-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online + Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | KBSE |
| Conference Code | 2020-11-KBSE-SC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automation of Ontology Generation by Pre-trained Language Model |
| Sub Title (in English) | |
| Keyword(1) | ontology |
| Keyword(2) | neural network |
| Keyword(3) | pre-trained model |
| Keyword(4) | natural language processing |
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| 1st Author's Name | Atusi Oba |
| 1st Author's Affiliation | the University of Aizu Graduate School (UoA) |
| 2nd Author's Name | Ayato Kuwana |
| 2nd Author's Affiliation | the University of Aizu Graduate School (UoA) |
| 3rd Author's Name | Paik Incheon |
| 3rd Author's Affiliation | the University of Aizu (UoA) |
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| Speaker | Author-2 |
| Date Time | 2020-11-13 15:22:00 |
| Presentation Time | 2 minutes |
| Registration for | KBSE |
| Paper # | KBSE2020-22, SC2020-26 |
| Volume (vol) | vol.120 |
| Number (no) | no.231(KBSE), no.232(SC) |
| Page | p.40 |
| #Pages | 1 |
| Date of Issue | 2020-11-06 (KBSE, SC) |
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