NORMAS at SemEval-2016 task 1: Semsim: A multi-feature approach to semantic text similarity

Abstract

This paper presents the submission of our team (NORMAS) to the SemEval 2016 semantic 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. Features extracted include lexical, syntactic and semantic features. This paper describes the features we designed for assessing the semantic similarity between sentence pairs, the models we build using these features and the performance obtained by the resulting systems on the 2016 evaluation data.