@inproceedings{2cf8a36845dc4404810df51e1bd92c9a,
title = "ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements",
abstract = "This paper reports our submissions to the Semantic Textual Similarity (STS) task in SEM Shared Task 2013. We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.",
author = "Zhu, \{Tian Tian\} and Man Lan",
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 = "英语",
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 = "124--131",
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",
address = "澳大利亚",
}