Name
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 81p and a precision of 78p. 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2800786 | ACM Trans. Asian & Low-Resource Lang. Inf. Process. |
Keywords | Field | DocType |
Patterns matching,Arabic discourse relations,causal relations,information extraction | Computer science,Popularity,Causation,Discourse analysis,Information extraction,Natural language processing,Artificial intelligence,Sentence,Recall,Linguistics,Syntax,Speech technology | Journal |
Volume | Issue | ISSN |
15 | 3 | 2375-4699 |
Citations | PageRank | References |
5 | 0.52 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jawad Sadek | 1 | 14 | 2.20 |
| Farid Meziane | 2 | 308 | 37.98 |