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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 114-120 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.493

Boosting the Efficiency of First-Order Abductive Reasoning Using Pre-estimated Relatedness between Predicates

Kazeto Yamamoto, Naoya Inoue, Kentaro Inui, Yuki Arase and Jun’ichi Tsujii
Abstract—Abduction is inference to the best explanation. While abduction has long been considered a promising framework for natural language processing (NLP), its computational complexity hinders its application to practical NLP problems. In this paper, we propose a method to predetermine the semantic relatedness between predicates and to use that information to boost the efficiency of first-order abductive reasoning. The proposed method uses the estimated semantic relatedness as follows: (i) to block inferences leading to explanations that are semantically irrelevant to the observations, and (ii) to cluster semantically relevant observations in order to split the task of abduction into a set of non-interdependent subproblems that can be solved in parallel. Our experiment with a large-scale knowledge base for a real-life NLP task reveals that the proposed method drastically reduces the size of the search space and significantly improves the computational efficiency of first-order abductive reasoning compared with the state-of-the-art system.

Index Terms—Natural language processing, logical inference, abduction.

Kazeto Yamamoto, Naoya Inoue, and Kentaro Inui are with Tohoku University, Japan (e-mail: {kazeto,naoya-i,inui}@cl.ecei.tohoku.ac.jp).
Yuki Arase is with Osaka University, Japan (e-mail: arase@ist.osaka-u.ac.jp).
Jun’ichi Tsujii is with Microsoft Research Asia, China (e-mail: jtsujii@microsoft.com).

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Cite: Kazeto Yamamoto, Naoya Inoue, Kentaro Inui, Yuki Arase and Jun’ichi Tsujii, "Boosting the Efficiency of First-Order Abductive Reasoning Using Pre-estimated Relatedness between Predicates," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 114-120, 2015.

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