This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensem-bling both models. Out of 84 submitted runs, our team best multi-lingual ran has been ranked 12~(th) in overall performance with correlation of 0.61, 7~(th) among 31 participating teams.
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