Paper Info
Title
Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features.
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.
Year
DOI
Venue
2017
10.1016/j.ipm.2017.01.002
Inf. Process. Manage.
Keywords
Field
DocType
Paraphrase identification,Semantic text similarity,Semantic analysis,Arabic language,Natural language processing
Semantic similarity,Information retrieval,Computer science,Support vector machine,Explicit semantic analysis,Paraphrase,Artificial intelligence,Natural language processing,Principle of maximum entropy,Syntax,Semantic computing,Text processing
Journal
Volume
Issue
ISSN
53
3
0306-4573
Citations 
PageRank 
References 
13
0.65
47
Authors
4
Name
Order
Citations
PageRank
Mohammad Al-Smadi118821.02
Zain Jaradat2130.65
Mahmoud Al-Ayyoub373063.41
Yaser Jararweh496888.95