Paper: UNITOR-CORE_TYPED: Combining Text Similarity and Semantic Filters through SV Regression

ACL ID S13-1007
Title UNITOR-CORE_TYPED: Combining Text Similarity and Semantic Filters through SV Regression
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper presents the UNITOR system that participated in the *SEM 2013 shared task on Semantic Textual Similarity (STS). The task is modeled as a Support Vector (SV) regression problem, where a similarity scoring function between text pairs is acquired from examples. The proposed approach has been implemented in a system that aims at providing high ap- plicability and robustness, in order to reduce the risk of over-fitting over a specific datasets. Moreover, the approach does not require any manually coded resource (e.g. WordNet), but mainly exploits distributional analysis of un- labeled corpora. A good level of accuracy is achieved over the shared task: in the Typed STS task the proposed system ranks in 1st and 2nd position.