UNITOR: Combining Semantic Text Similarity functions through SV Regression
Danilo Croce, Paolo Annesi, Valerio Storch, Roberto Basili · 2012
This paper presents the UNITOR system that participated to the SemEval 2012 Task 6: Se-mantic Textual Similarity (STS). The task is here modeled as a Support Vector (SV) regres-sion problem, where a similarity scoring func-tion between text pairs is acquired from exam-ples. The semantic relatedness between sen-tences 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 ef-fectively combines the different models, learn-ing 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. 1