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.
Croce, D., Annesi, P., Storch, V., Basili, R. (2012). UNITOR: Combining semantic text similarity functions through SV Regression. In SEM 2012: 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 (SemEval 2012) (pp.597-602). Association for Computational Linguistics (ACL).
UNITOR: Combining semantic text similarity functions through SV Regression
Croce D.;Basili R.
2012-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
