@inproceedings{5eaee896897e427a85eadcf2da756969,
title = "MayoClinicNLP-CORE: Semantic representations for textual similarity",
abstract = "The Semantic Textual Similarity (STS) task examines semantic similarity at a sentencelevel. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used sourcespecific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.",
author = "Stephen Wu and Dongqing Zhu and Ben Carterette and Hongfang Liu",
note = "Publisher Copyright: {\textcopyright}2013 Association for Computational Linguistics.; 2nd Joint Conference on Lexical and Computational Semantics, SEM 2013 ; Conference date: 13-06-2013 Through 14-06-2013",
year = "2013",
language = "English (US)",
series = "SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics, Proceedings of the Main Conference and the Shared Task: Semantic Textual SimilaritySEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics, Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity",
publisher = "Association for Computational Linguistics (ACL)",
pages = "148--154",
editor = "Mona Diab and Tim Baldwin and Marco Baroni",
booktitle = "SEM 2013 - 2nd Joint Conference on Lexical and Computational Semantics, Proceedings of the Main Conference and the Shared Task",
}