Abstract
This paper describes the system for rating the degree of semantic equivalence between two text snippets developed by IHS-RD-Belarus for the SemEval 2016 ST S shared task (Task 1). To predict the human ratings of text similarity we use a support vector regression model with multiple features representing similarity and difference scores calculated for each pair of sentences.
Cite
CITATION STYLE
Beliuha, M., & Chernyshevich, M. (2016). IHS-RD-Belarus at SemEval-2016 task 1: Multistage approach for measuring semantic similarity. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 696–701). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1107
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