We describe a set of top-performing systems at the SemEval 2015 English Semantic Textual Similarity (STS) task. Given two English sentences, each system outputs the degree of their semantic similarity. Our unsupervised system, which is based on word alignments across the two input sentences, ranked 5th among 73 submitted system runs with a mean correlation of 79.19% with human annotations. We also submitted two runs of a supervised system which uses word alignments and similarities between compositional sentence vectors as its features. Our best supervised run ranked 1st with a mean correlation of 80.15%.
It REF again achieved the best result as shown at STS 2015 us- ing word alignment and similarities between compositional sentence vectors as its features.
DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector Composition
SemEval@NAACL-HLT
Computer Science