NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity

This paper presents the submission of our team (NORMAS) to the SemEval 2016 se- mantic textual similarity (STS) shared task. We submitted three system runs, each using a set of 36 features extracted from the training set. The runs explore the use of the following three machine learning algorithms: Support Vector Regression, Elastic Net and Random Forest. Each run was trained using sentence pairs from the STS 2012 training data. Fea- tures extracted include lexical, syntactic and semantic features. This paper describes the features we designed for assessing the seman- tic similarity between sentence pairs, the mod- els we build using these features and the per- formance obtained by the resulting systems on the 2016 evaluation data.