UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

Tomáš Brychcín and Lukáš Svoboda
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (2016)
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Abstract

We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods benefit from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked first among 26 systems.

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