Sentence similarity by combining explicit semantic analysis and overlapping n-grams
|Sentence similarity by combining explicit semantic analysis and overlapping n-grams|
|Author(s)||Vu H.H., Villaneau J., Said F., Marteau P.-F.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Knowledge based systems, Explicit semantic analysis, N-grams, Sentence similarity, Similarity measure, Similarity scores, Wikipedia, Semantics)|
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Sentence similarity by combining explicit semantic analysis and overlapping n-grams is a 2014 conference paper written in English by Vu H.H., Villaneau J., Said F., Marteau P.-F. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
We propose a similarity measure between sentences which combines a knowledge-based measure, that is a lighter version of ESA (Explicit Semantic Analysis), and a distributional measure, Rouge. We used this hybrid measure with two French domain-orientated corpora collected from the Web and we compared its similarity scores to those of human judges. In both domains, ESA and Rouge perform better when they are mixed than they do individually. Besides, using the whole Wikipedia base in ESA did not prove necessary since the best results were obtained with a low number of well selected concepts.
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