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

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.

• Publication year

  2016

• Venue

  International Workshop on Semantic Evaluation

• Publication date

  2016-06-01

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/v1/S16-1089
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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