Naslov
TakeLab: Systems for Measuring Semantic Text Similarity

Autori
Šarić, Frane ; Glavaš, Goran ; Karan, Mladen ; Šnajder, Jan ; Dalbelo Bašić, Bojana

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
*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), 2012, 441-448

Skup
*SEM 2012: Joint Conference on Lexical and Computational Semantics

Mjesto i datum
Montréal, Kanada, 07.06.2012. - 08.06.2012

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
textual similarity ; semantic similarity ; machine learning

Sažetak
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).

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA