UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks

We introduce a system focused on solving SemEval 2016 Task 2 – Interpretable Semantic Textual Similarity. The system explores machine learning and rule-based approaches to the task. We focus on machine learning and experiment with a wide variety of machine learning algorithms as well as with several types of features. The core of our system consists in exploiting distributional semantics to compare similarity of sentence chunks. The system won the competition in 2016 in the “Gold standard chunk scenario”. We have not participated in the “System chunk scenario”.