Affiliation:
1. University of Salford, The Crescent, UK
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.
Publisher
Association for Computing Machinery (ACM)
Reference37 articles.
1. On the evaluation and improvement of Arabic WordNet coverage and usability
2. Arabic newspapers discourse: Rhetorical features, discourse markers, strategies and organization;Al-Jarrah Mohammad;Damascus University Journal,2011
3. CAUSAL LINKING IN SPOKEN AND WRITTEN ENGLISH
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