research-article

Recognizing Inference in Texts with Markov Logic Networks

Authors:

Xuanjing HuangAuthors Info & Claims

ACM Transactions on Asian Language Information Processing (TALIP), Volume 11, Issue 4

Article No.: 15, Pages 1 - 23

https://doi.org/10.1145/2382593.2382597

Published: 01 December 2012 Publication History

Abstract

Recognizing inference in texts (RITE) attracts growing attention of natural language processing (NLP) researchers in recent years. In this article, we propose a novel approach to recognize inference with probabilistic logical reasoning. Our approach is built on Markov logic networks (MLNs) framework, which is a probabilistic extension of first-order logic. We design specific semantic rules based on the surface, syntactic, and semantic representations of texts, and map these rules to logical representations. We also extract information from some knowledge bases as common sense logic rules. Then we utilize MLNs framework to make predictions with combining statistical and logical reasoning. Experiment results shows that our system can achieve better performance than state-of-the-art RITE systems.

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Cited By

Putra ISiahaan DSaikhu A(2024)Recognizing textual entailment: A review of resources, approaches, applications, and challengesICT Express10.1016/j.icte.2023.08.01210:1(132-155)Online publication date: Feb-2024
https://doi.org/10.1016/j.icte.2023.08.012
Biba MVajjhala N(2022)Statistical Relational Learning for Genomics Applications: A State-of-the-Art ReviewHandbook of Machine Learning Applications for Genomics10.1007/978-981-16-9158-4_3(31-42)Online publication date: 24-Jun-2022
https://doi.org/10.1007/978-981-16-9158-4_3

Index Terms

Recognizing Inference in Texts with Markov Logic Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Transactions on Asian Language Information Processing

ACM Transactions on Asian Language Information Processing Volume 11, Issue 4

Special Issue on RITE

December 2012

130 pages

ISSN:1530-0226

EISSN:1558-3430

DOI:10.1145/2382593

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Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 December 2012

Accepted: 01 September 2012

Revised: 01 July 2012

Received: 01 May 2012

Published in TALIP Volume 11, Issue 4

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Cited By

Putra ISiahaan DSaikhu A(2024)Recognizing textual entailment: A review of resources, approaches, applications, and challengesICT Express10.1016/j.icte.2023.08.01210:1(132-155)Online publication date: Feb-2024
https://doi.org/10.1016/j.icte.2023.08.012
Biba MVajjhala N(2022)Statistical Relational Learning for Genomics Applications: A State-of-the-Art ReviewHandbook of Machine Learning Applications for Genomics10.1007/978-981-16-9158-4_3(31-42)Online publication date: 24-Jun-2022
https://doi.org/10.1007/978-981-16-9158-4_3

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