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| Paper Abstract and Keywords | ||
| Presentation | 2021-09-16 13:00 Ensemble BERT-BiLSTM-CNN Model for Sequence Classification Vuong Thi Hong (NII/SOKENDAI), Takasu Atsuhiro (NII)  DE2021-12 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Ensemble methods use multiple learning algorithms to obtain better predictive performance. Currently, deep learning models with multilayer processing architecture are showed that the performance is better than the traditional classification models. Ensemble deep learning models combine the advantages of both ensemble learning and deep learning such that the final model has better performance. This paper presents a novel ensemble deep learning method, achieving robust and effective sequence classification facing sparse data. We use the BERT (Bidirectional Encoder Representation from Transformers) as the word embedding method. Then, we integrate the BiLSTM (Bidirectional Long Short-Term Memory) and CNN (Convolutional Neural Network) with an attention mechanism for sequence classification. We evaluate our ensemble models with two datasets with the different baseline methods. The first dataset is from IMDB and contains 50,000 movie reviews, labeled with two sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments for six classes. The experimental results show that our proposed method provides an accurate, reliable, and effective solution for sequence data classification. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Ensemble deep learning / Sequence classification / BERT / BiLSTM / CNN / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 176, DE2021-12, pp. 1-6, Sept. 2021. | |
| Paper # | DE2021-12 | |
| Date of Issue | 2021-09-09 (DE) | |
| 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 | DE2021-12 |
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| Conference Information | |
| Committee | DE IPSJ-DBS IPSJ-IFAT |
| Conference Date | 2021-09-16 - 2021-09-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Management, information retrieval, knowledge acquisition and general for big data |
| Paper Information | |
| Registration To | DE |
| Conference Code | 2021-09-DE-DBS-IFAT |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Ensemble BERT-BiLSTM-CNN Model for Sequence Classification |
| Sub Title (in English) | |
| Keyword(1) | Ensemble deep learning |
| Keyword(2) | Sequence classification |
| Keyword(3) | BERT |
| Keyword(4) | BiLSTM |
| Keyword(5) | CNN |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Vuong Thi Hong |
| 1st Author's Affiliation | National Institute of Informatics/SOKENDAI (NII/SOKENDAI) |
| 2nd Author's Name | Takasu Atsuhiro |
| 2nd Author's Affiliation | National Institute of Informatics (NII) |
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| Speaker | Author-1 |
| Date Time | 2021-09-16 13:00:00 |
| Presentation Time | 30 minutes |
| Registration for | DE |
| Paper # | DE2021-12 |
| Volume (vol) | vol.121 |
| Number (no) | no.176 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2021-09-09 (DE) |
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