research-article

Extracting Arabic Causal Relations Using Linguistic Patterns

Authors:

Farid MezianeAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 15, Issue 3

Article No.: 14, Pages 1 - 20

https://doi.org/10.1145/2800786

Published: 08 March 2016 Publication History

Abstract

Identifying semantic relations is a crucial step in discourse analysis and is useful for many applications in both language and speech technology. Automatic detection of Causal relations therefore has gained popularity in the literature within different frameworks. The aim of this article is the automatic detection and extraction of Causal relations that are explicitly expressed in Arabic texts. To fulfill this goal, a Pattern Recognizer model was developed to signal the presence of cause--effect information within sentences from nonspecific domain texts. This model incorporates approximately 700 linguistic patterns so that parts of the sentence representing the cause and those representing the effect can be distinguished. The patterns were constructed based on different sets of syntactic features by analyzing a large untagged Arabic corpus. In addition, the model was boosted with three independent algorithms to deal with certain types of grammatical particles that indicate causation. With this approach, the proposed model achieved an overall recall of 81% and a precision of 78%. Evaluation results revealed that the justification particles play a key role in detecting Causal relations. To the best of our knowledge, no previous studies have been dedicated to dealing with this type of relation in the Arabic language.

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Cited By

Xiang GZhang YTan JRan ZShi E(2023)Research on the construction of event corpus with document-level causal relations for social securityInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10351560:6Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103515
Izumi KSakaji H(2020)Economic Causal-Chain Search Using Text Mining TechnologyArtificial Intelligence. IJCAI 2019 International Workshops10.1007/978-3-030-56150-5_2(23-35)Online publication date: 18-Aug-2020
https://doi.org/10.1007/978-3-030-56150-5_2
Ghoniem RAlhelwa NShaalan K(2019)A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic TextAlgorithms10.3390/a1209018212:9(182)Online publication date: 29-Aug-2019
https://doi.org/10.3390/a12090182
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Index Terms

Extracting Arabic Causal Relations Using Linguistic Patterns
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 15, Issue 3

March 2016

220 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/2876004

Editor:
Richard Sproat
Google, Inc., USA

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Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2016

Accepted: 01 January 2015

Received: 01 June 2014

Published in TALLIP Volume 15, Issue 3

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Cited By

Xiang GZhang YTan JRan ZShi E(2023)Research on the construction of event corpus with document-level causal relations for social securityInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10351560:6Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.ipm.2023.103515
Izumi KSakaji H(2020)Economic Causal-Chain Search Using Text Mining TechnologyArtificial Intelligence. IJCAI 2019 International Workshops10.1007/978-3-030-56150-5_2(23-35)Online publication date: 18-Aug-2020
https://doi.org/10.1007/978-3-030-56150-5_2
Ghoniem RAlhelwa NShaalan K(2019)A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic TextAlgorithms10.3390/a1209018212:9(182)Online publication date: 29-Aug-2019
https://doi.org/10.3390/a12090182
Tanaka YKodera SSato FSakaji HIzumi K(2019)The Extraction of the Future-Oriented Sentences from Annual Reports2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI.2019.00140(679-684)Online publication date: Jul-2019
https://doi.org/10.1109/IIAI-AAI.2019.00140
Niki YSakaji HIzumi KMatsushima H(2019)Causality Existence Classification from Multilingual Texts Using End-to-End LSTM Models2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00011(17-23)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00011
Sakaji HMurono RSakai HBennett JIzumi K(2017)Discovery of rare causal knowledge from financial statement summaries2017 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2017.8285265(1-7)Online publication date: Nov-2017
https://doi.org/10.1109/SSCI.2017.8285265
Mezghanni IGargouri F(2017)Deriving ontological semantic relations between Arabic compound nouns conceptsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2017.03.00129:2(212-228)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.jksuci.2017.03.001
Hassani HHuang XGhodsi M(2017)Big Data and CausalityAnnals of Data Science10.1007/s40745-017-0122-35:2(133-156)Online publication date: 1-Aug-2017
https://doi.org/10.1007/s40745-017-0122-3
Sadek JMeziane F(2016)A Discourse-Based Approach for Arabic Question AnsweringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/298823816:2(1-18)Online publication date: 4-Nov-2016
https://dl.acm.org/doi/10.1145/2988238

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