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Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity
Computer Science and Information Systems, Tome 12 (2015) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.
Keywords: short-text semantic similarity, statistical similarity, corpus-based measures, part-of-speech tags, POS weighting, syntactic information, bag-of-words model, natural language processing 
@article{CSIS_2015_12_1_a1,
     author = {Vuk Batanovi\'c and Dragan Boji\'c},
     title = {Using {Part-of-Speech} {Tags} as {Deep-Syntax} {Indicators} in {Determining} {Short-Text} {Semantic} {Similarity}},
     journal = {Computer Science and Information Systems},
     year = {2015},
     volume = {12},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2015_12_1_a1/}
}
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Vuk Batanović; Dragan Bojić. Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity. Computer Science and Information Systems, Tome 12 (2015) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2015_12_1_a1/