UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks
Miloslav Konopík
and
Tomáš Brychcín
and
Ondřej Pražák
and
David Steinberger
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (2016)
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Abstract
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”.