TakeLab: Systems for Measuring Semantic Text Similarity (CROSBI ID 587433)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Šarić, Frane ; Glavaš, Goran ; Karan, Mladen ; Šnajder, Jan ; Dalbelo Bašić, Bojana
engleski
TakeLab: Systems for Measuring Semantic Text Similarity
This paper describes the two systems for determining the semantic similarity of short texts submitted to the SemEval 2012 Task 6. Most of the research on semantic similarity of textual content focuses on large documents. However, a fair amount of information is condensed into short text snippets such as social media posts, image captions, and scientific abstracts. We predict the human ratings of sentence similarity using a support vector regression model with multiple features measuring word-overlap similarity and syntax similarity. Out of 89 systems submitted, our two systems ranked in the top 5, for the three overall evaluation metrics used (overall Pearson – 2nd and 3rd, normalized Pearson – 1st and 3rd, weighted mean – 2nd and 5th).
textual similarity ; semantic similarity ; machine learning
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Podaci o prilogu
441-448.
2012.
objavljeno
Podaci o matičnoj publikaciji
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics
Agirre, Eneko ; Bos, Johan ; Diab, Mona ; Manandhar, Suresh ; Marton, Yuval ; Yuret, Deniz
Montréal: Association for Computational Linguistics (ACL)
Podaci o skupu
*SEM 2012: Joint Conference on Lexical and Computational Semantics
predavanje
07.06.2012-08.06.2012
Montréal, Kanada