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Paper Abstract and Keywords
Presentation 2023-03-17 13:30
Investigation of similarity between synonyms using sentence vectors generated by Sentence-BERT
Masato Izumi, Kenya Jin'no (Tokyo City Univ.) MSS2022-101 NLP2022-146
Abstract (in Japanese) (See Japanese page) 
(in English) The sentence vector output by Sentence-BERT has 768 dimensions. These 768 dimensions cannot be completely divided into words, but they are divided to some extent. In this study, we investigate how similar each dimension of the output sentence vector is when sentences with similar meanings or sentences containing the same words are inputted, and how the dimension representing the target word in the sentence vector changes when the sentence vector is output by embedding words that had similar values in the distributed representation of the words in the sentences.
Keyword (in Japanese) (See Japanese page) 
(in English) Sentence-BERT / UMAP / Sentence Vector / Latent Variable / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 436, NLP2022-146, pp. 182-185, March 2023.
Paper # NLP2022-146 
Date of Issue 2023-03-08 (MSS, NLP) 
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 MSS2022-101 NLP2022-146

Conference Information
Committee NLP MSS  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2023-03-NLP-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of similarity between synonyms using sentence vectors generated by Sentence-BERT 
Sub Title (in English)  
Keyword(1) Sentence-BERT  
Keyword(2) UMAP  
Keyword(3) Sentence Vector  
Keyword(4) Latent Variable  
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1st Author's Name Masato Izumi  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2023-03-17 13:30:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2022-101, NLP2022-146 
Volume (vol) vol.122 
Number (no) no.435(MSS), no.436(NLP) 
Page pp.182-185 
#Pages 4 
Date of Issue 2023-03-08 (MSS, NLP) 

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