@inproceedings{62c5a22cb01c4d1d82bc391d7d732e2a,
title = "Penn: Using word similarities to better estimate sentence similarity",
abstract = "We present the Penn system for SemEval-2012 Task 6, computing the degree of semantic equivalence between two sentences. We explore the contributions of different vector models for computing sentence and word similarity: Collobert and Weston embeddings as well as two novel approaches, namely eigenwords and selectors. These embeddings provide different measures of distributional similarity between words, and their contexts. We used regression to combine the different similarity measures, and found that each provides partially independent predictive signal above baseline models.",
author = "Sneha Jha and \{Andrew Schwartz\}, H. and Ungar, \{Lyle H.\}",
note = "Publisher Copyright: {\textcopyright} 2012 Association for Computational Linguistics.; 1st Joint Conference on Lexical and Computational Semantics, *SEM 2012 ; Conference date: 07-06-2012 Through 08-06-2012",
year = "2012",
language = "English",
series = "*SEM 2012 - 1st Joint Conference on Lexical and Computational Semantics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "679--683",
booktitle = "Proceedings of the 6th International Workshop on Semantic Evaluation, SemEval 2012",
}