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| Presentation | 2018-09-06 17:20 Latent co-occurrence words graph extraction using sparse structure estimation -- Comparison of word vectors between topic model and distributed representation -- Norimitsu Kubono, Nozomi Hiyoshi, Daiju Akashi (PERSOL CAREER)  NLC2018-19 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We are considering application of "structural topic model" in order to extract customer insight from member questionnaire on job change site DODA. As a new method of extracting customer insight, we investigated extracting latent co-occurrence graph based on sparse structure estimation method from word co-occurrence graph. We made a word vector from four types of topic model (structural topic model, LDA) and distributed representation (word 2 vec, Glove) and evaluated using sparsity estimation regularization parameters and graph cluster. Furthermore, we interpreted the graph cluster as a topic, defined document vectors, and also applied feasibility study to document clustering. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Customer Insight / Structural Topic Model / co-occurrence graph / sparse structure learning / Graphical Lasso / feature selection / graph clustering / document clustering | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 210, NLC2018-19, pp. 51-56, Sept. 2018. | |
| Paper # | NLC2018-19 | |
| Date of Issue | 2018-08-30 (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 | NLC2018-19 |
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| Conference Information | |
| Committee | NLC IPSJ-DC |
| Conference Date | 2018-09-06 - 2018-09-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Seikei University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | The Thirteenth Text Analytics Symposium |
| Paper Information | |
| Registration To | NLC |
| Conference Code | 2018-09-NLC-DC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Latent co-occurrence words graph extraction using sparse structure estimation |
| Sub Title (in English) | Comparison of word vectors between topic model and distributed representation |
| Keyword(1) | Customer Insight |
| Keyword(2) | Structural Topic Model |
| Keyword(3) | co-occurrence graph |
| Keyword(4) | sparse structure learning |
| Keyword(5) | Graphical Lasso |
| Keyword(6) | feature selection |
| Keyword(7) | graph clustering |
| Keyword(8) | document clustering |
| 1st Author's Name | Norimitsu Kubono |
| 1st Author's Affiliation | PERSOL CAREER (PERSOL CAREER) |
| 2nd Author's Name | Nozomi Hiyoshi |
| 2nd Author's Affiliation | PERSOL CAREER (PERSOL CAREER) |
| 3rd Author's Name | Daiju Akashi |
| 3rd Author's Affiliation | PERSOL CAREER (PERSOL CAREER) |
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| Speaker | Author-1 |
| Date Time | 2018-09-06 17:20:00 |
| Presentation Time | 25 minutes |
| Registration for | NLC |
| Paper # | NLC2018-19 |
| Volume (vol) | vol.118 |
| Number (no) | no.210 |
| Page | pp.51-56 |
| #Pages | 6 |
| Date of Issue | 2018-08-30 (NLC) |
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