@inproceedings{bfc33475647e461aaa79ac6435725695,
title = "DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition",
abstract = "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%.",
author = "Sultan, {Md Arafat} and Steven Bethard and Tamara Sumner",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics; 9th International Workshop on Semantic Evaluation, SemEval 2015 ; Conference date: 04-06-2015 Through 05-06-2015",
year = "2015",
language = "English (US)",
series = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "148--153",
editor = "Preslav Nakov and Torsten Zesch and Daniel Cer and David Jurgens",
booktitle = "SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics",
}