University_of_Sheffield: two approaches to semantic text similarity

Authors:
Sam Biggins;Shaabi Mohammed;Sam Oakley;Luke Stringer;Mark Stevenson;Judita Priess
Affiliations:
University of Sheffield Sheffield, UK;University of Sheffield Sheffield, UK;University of Sheffield Sheffield, UK;University of Sheffield Sheffield, UK;University of Sheffield Sheffield, UK;University of Sheffield Sheffield, UK
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 the University of Sheffield's submission to SemEval-2012 Task 6: Semantic Text Similarity. Two approaches were developed. The first is an unsupervised technique based on the widely used vector space model and information from WordNet. The second method relies on supervised machine learning and represents each sentence as a set of n-grams. This approach also makes use of information from WordNet. Results from the formal evaluation show that both approaches are useful for determining the similarity in meaning between pairs of sentences with the best performance being obtained by the supervised approach. Incorporating information from WordNet also improves performance for both approaches.