Paper: UNIBA-CORE: Combining Strategies for Semantic Textual Similarity

ACL ID S13-1024
Title UNIBA-CORE: Combining Strategies for Semantic Textual Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper describes the UNIBA participation in the Semantic Textual Similarity (STS) core task 2013. We exploited three different sys- tems for computing the similarity between two texts. A system is used as baseline, which rep- resents the best model emerged from our pre- vious participation in STS 2012. Such system is based on a distributional model of seman- tics capable of taking into account also syn- tactic structures that glue words together. In addition, we investigated the use of two dif- ferent learning strategies exploiting both syn- tactic and semantic features. The former uses ensemble learning in order to combine the best machine learning techniques trained on 2012 training and test sets. The latter tries to overcome the limit of working with different datasets with varying ...