DLS$@$CU: Sentence Similarity from Word Alignment and Semantic Vector Composition

Md Arafat Sultan, Steven Bethard, Tamara Sumner
2015 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)  
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
more » ... sitional sentence vectors as its features. Our best supervised run ranked 1st with a mean correlation of 80.15%.
doi:10.18653/v1/s15-2027 dblp:conf/semeval/SultanBS15 fatcat:jvf2lifs3reyrk5j2zwfuwicgm