Paper: DLS-CORE: A Simple Machine Learning Model of Semantic Textual Similarity

ACL ID S13-1025
Title DLS-CORE: A Simple Machine Learning Model of Semantic Textual Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

We present a system submitted in the Semantic Textual Similarity (STS) task at the Second Joint Conference on Lexical and Computa- tional Semantics (*SEM 2013). Given two short text fragments, the goal of the system is to determine their semantic similarity. Our sys- tem makes use of three different measures of text similarity: word n-gram overlap, character n-gram overlap and semantic overlap. Using these measures as features, it trains a support vector regression model on SemEval STS 2012 data. This model is then applied on the STS 2013 data to compute textual similarities. Two different selections of training data result in very different performance levels: while a cor- relation of 0.4135 with gold standards was ob- served in the official evaluation (ranked 63rd among all ...