Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features. Issue 3 (May 2017)

• Record Type: Journal Article
• Authors: AL-Smadi, Mohammad; Jaradat, Zain; AL-Ayyoub, Mahmoud; Jararweh, Yaser
• Is Part Of: Information processing & management. Volume 53:Issue 3(2017:May)
• Page Start: 640
• Page End: 652
• Publication Date: 2017-05
• Subjects: Paraphrase identification -- Semantic text similarity -- Semantic analysis -- Arabic language -- Natural language processing; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2017.01.002
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: The rapid growth in digital information has raised considerable challenges in particular when it comes to automated content analysis. Social media such as twitter share a lot of its users' information about their events, opinions, personalities, etc. Paraphrase Identification (PI) is concerned with recognizing whether two texts have the same/similar meaning, whereas the Semantic Text Similarity (STS) is concerned with the degree of that similarity. This research proposes a state-of-the-art approach for paraphrase identification and semantic text similarity analysis in Arabic news tweets. The approach adopts several phases of text processing, features extraction and text classification. Lexical, syntactic, and semantic features are extracted to overcome the weakness and limitations of the current technologies in solving these tasks for the Arabic language. Maximum Entropy (MaxEnt) and Support Vector Regression (SVR) classifiers are trained using these features and are evaluated using a dataset prepared for this research. The experimentation results show that the approach achieves good results in comparison to the baseline results.