Saarland: vector-based models of semantic textual similarity

Authors:
Georgiana Dinu;Stefan Thater
Affiliations:
Center of Mind/Brain Sciences University of Trento;Universität des Saarlandes
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 describes our system for the Semeval 2012 Sentence Textual Similarity task. The system is based on a combination of few simple vector space-based methods for word meaning similarity. Evaluation results show that a simple combination of these unsupervised data-driven methods can be quite successful. The simple vector space components achieve high performance on short sentences; on longer, more complex sentences, they are outperformed by a surprisingly competitive word overlap baseline, but they still bring improvements over this baseline when incorporated into a mixture model.