UNITOR: combining semantic text similarity functions through SV regression

Authors:
Danilo Croce;Paolo Annesi;Valerio Storch;Roberto Basili
Affiliations:
University of Roma, Tor Vergata Roma, Italy;University of Roma, Tor Vergata Roma, Italy;University of Roma, Tor Vergata Roma, Italy;University of Roma, Tor Vergata Roma, Italy
Venue:
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Year:
2012

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Abstract

This paper presents the UNITOR system that participated to the SemEval 2012 Task 6: Semantic Textual Similarity (STS). The task is here modeled as a Support Vector (SV) regression problem, where a similarity scoring function between text pairs is acquired from examples. The semantic relatedness between sentences is modeled in an unsupervised fashion through different similarity functions, each capturing a specific semantic aspect of the STS, e. g. syntactic vs. lexical or topical vs. paradigmatic similarity. The SV regressor effectively combines the different models, learning a scoring function that weights individual scores in a unique resulting STS. It provides a highly portable method as it does not depend on any manually built resource (e.g. WordNet) nor controlled, e. g. aligned, corpus.