UNIBA: Distributional Semantics for Textual Similarity
Annalina Caputo, Pierpaolo Basile, Giovanni Maria Semeraro · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2012
We report the results of UNIBA participation in the first SemEval-2012 Semantic Textual Similarity task. Our systems rely on distributional models of words automatically inferred from a large corpus. We exploit three different semantic word spaces: Random Indexing (RI), Latent Semantic Analysis (LSA) over RI, and vector permutations in RI. Runs based on these spaces consistently outperform the baseline on the proposed datasets.