UNT: a supervised synergistic approach to semantic text similarity

Authors:
Carmen Banea;Samer Hassan;Michael Mohler;Rada Mihalcea
Affiliations:
University of North Texas Denton, TX;University of North Texas Denton, TX;University of North Texas Denton, TX;University of North Texas Denton, TX
Venue:
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Year:
2012

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Abstract

This paper presents the systems that we participated with in the Semantic Text Similarity task at SEMEVAL 2012. Based on prior research in semantic similarity and relatedness, we combine various methods in a machine learning framework. The three variations submitted during the task evaluation period ranked number 5, 9 and 14 among the 89 participating systems. Our evaluations show that corpus-based methods display a more robust behavior on the training data, yet combining a variety of methods allows a learning algorithm to achieve a superior decision than that achievable by any of the individual parts.