Extracting Arabic causal relations using linguistic patterns

Journal article

Sadek, J and Meziane, F. 2016. Extracting Arabic causal relations using linguistic patterns. ACM Transactions on Asian and Low-Resource Language Information Processing. Vol 15 (Issue 3, Article: 14), p. 1–20. https://doi.org/10.1145/2800786
AuthorsSadek, J and Meziane, F.
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 paper 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 non-specific 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.

KeywordsAnd Phrases; Patterns matching; Arabic discourse relations; Causal relations; Information extraction
Year2016
JournalACM Transactions on Asian and Low-Resource Language Information Processing
Journal citationVol 15 (Issue 3, Article: 14), p. 1–20
PublisherAssociation for Computing Machinery
ISSN2375-4699
2375-4702
Digital Object Identifier (DOI)https://doi.org/10.1145/2800786
Web address (URL)http://dx.doi.org/10.1145/2800786
Output statusPublished
Publication dates
Online08 Mar 2016
Publication process dates
Accepted01 Jan 2015
Deposited05 Jun 2023
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