Paper Info
Title
Low-Level Features for Paraphrase Identification.
Abstract
This paper deals with the task of sentential paraphrase identification. We work with Russian but our approach can be applied to any other language with rich morphology and free word order. As part of our ParaPhraser.ru project, we construct a paraphrase corpus and then experiment with supervised methods of paraphrase identification. In this paper we focus on the low-level string, lexical and semantic features which unlike complex deep ones do not cause information noise and can serve as a solid basis for the development of an effective paraphrase identification system. Results of the experiments show that the features introduced in this paper improve the paraphrase identification model based solely on the standard low-level features or the optimized matrix metric used for corpus construction.
Year
DOI
Venue
2015
10.1007/978-3-319-27060-9_5
ADVANCES IN ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, MICAI 2015, PT I
Keywords
Field
DocType
Paraphrase identification,Low-level feature,Lexical feature,Semantic feature,Matrix similarity metric
Word order,Pattern recognition,Computer science,Identification system,Paraphrase,Natural language processing,Artificial intelligence,Semantic feature
Conference
Volume
ISSN
Citations 
9413
0302-9743
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Ekaterina Pronoza102.70
Elena Yagunova235.18