DLS$@$CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity
Sultan Sultan, Steven J. Bethard, Tamara Sumner · Joint Conference on Lexical and Computational Semantics · 2013
We present a system submitted in the Semantic Textual Similarity (STS) task at the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013). Given two short text fragments, the goal of the system is to determine their semantic similarity. Our system 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 correlation of 0.4135 with gold standards was observed in the official evaluation (ranked 63 among all systems) for one selection, the other resulted in a correlation of 0.5352 (that would rank 21).