Sadek, J and Meziane, F 2016, 'Extracting Arabic causal relations using linguistic patterns' , ACM Transactions on Asian and Low-Resource Language Information Processing, 15 (3) , 14:1-14:20.
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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.
|Uncontrolled Keywords:||Information systems, Information retrieval, Computing methodologies, Natural language processing, Patterns matching, Arabic discourse relations, Causal relations, Information extraction|
|Themes:||Media, Digital Technology and the Creative Economy|
|Schools:||Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)|
|Journal or Publication Title:||ACM Transactions on Asian and Low-Resource Language Information Processing|
|Publisher:||Association for Computing Machinery (ACM)|
|Funders:||Non funded research|
|Depositing User:||Prof Farid Meziane|
|Date Deposited:||01 Oct 2015 10:47|
|Last Modified:||11 Mar 2016 12:52|
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