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Paper Abstract and Keywords
Presentation 2023-03-14 11:50
Temporal relation identification toward generating temporal logic formulas
Maiko Onishi (Ochanomizu Univ.), Shinpei Ogata, Kozo Okano (Shinshu Univ.), Daisuke Bekki (Ochanomizu Univ.) SS2022-49
Abstract (in Japanese) (See Japanese page) 
(in English) There is room to utilize temporal relations in relation extraction that is incorporated in the analysis of requirements specifications. Several studies have employed rule-based relation extraction. For example, it is often incorporated into methods as a component of automatic generation of temporal logic formulas and state transition models. Since rules are created based on concrete examples, the scope of application tends to be limited. Therefore, it is necessary to be able to flexibly change the composition and performance of rule-based methods in response to increases or decreases in the scale of examples. However, it is not easy to increase the scalability and generality of methods in the current situation.
On the other hand, in the field of natural language processing, deep learning-based relation identification has been developed through TimeBank, which is a corpus annotated with temporal relations. But data in requirements engineering is not sufficiently large enough to learn relational identification. In addition, few studies have utilized deep learning-based relation extraction for challenging tasks in requirements engineering.
This study confirms the effectiveness of deep learning-based temporal relation identification when applied to tasks, where rule-based methods have been employed in the past. Using a news domain corpus annotated with temporal relations commonly employed in the field of natural language processing, we train a model to learn temporal relations between events in a sentence. The trained model is then used for fine tuning. Property specification patterns are a basic pattern matching method employed in the synthesis of temporal logic formulas. We applied this method to test data containing patterns that abstract requirement, and demonstrated its effectiveness.
Keyword (in Japanese) (See Japanese page) 
(in English) Software Engineering / Natural Language Processing / Temporal Relation Identification / Requirements Specification / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 432, SS2022-49, pp. 13-18, March 2023.
Paper # SS2022-49 
Date of Issue 2023-03-07 (SS) 
ISSN Online edition: ISSN 2432-6380
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)
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Conference Information
Committee SS  
Conference Date 2023-03-14 - 2023-03-15 
Place (in Japanese) (See Japanese page) 
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Paper Information
Registration To SS 
Conference Code 2023-03-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Temporal relation identification toward generating temporal logic formulas 
Sub Title (in English)  
Keyword(1) Software Engineering  
Keyword(2) Natural Language Processing  
Keyword(3) Temporal Relation Identification  
Keyword(4) Requirements Specification  
1st Author's Name Maiko Onishi  
1st Author's Affiliation Ochanomizu University (Ochanomizu Univ.)
2nd Author's Name Shinpei Ogata  
2nd Author's Affiliation Shinshu University (Shinshu Univ.)
3rd Author's Name Kozo Okano  
3rd Author's Affiliation Shinshu University (Shinshu Univ.)
4th Author's Name Daisuke Bekki  
4th Author's Affiliation Ochanomizu University (Ochanomizu Univ.)
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Speaker Author-1 
Date Time 2023-03-14 11:50:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2022-49 
Volume (vol) vol.122 
Number (no) no.432 
Page pp.13-18 
Date of Issue 2023-03-07 (SS) 

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