DeepPurple: estimating sentence semantic similarity using n-gram regression models and web snippets

  • Authors:

  • Nikos Malandrakis;Elias Iosif;Alexandros Potamianos

  • Affiliations:

  • Technical University of Crete, Chania, Greece;Technical University of Crete, Chania, Greece;Technical University of Crete, Chania, Greece

  • 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

We estimate the semantic similarity between two sentences using regression models with features: 1) n-gram hit rates (lexical matches) between sentences, 2) lexical semantic similarity between non-matching words, and 3) sentence length. Lexical semantic similarity is computed via co-occurrence counts on a corpus harvested from the web using a modified mutual information metric. State-of-the-art results are obtained for semantic similarity computation at the word level, however, the fusion of this information at the sentence level provides only moderate improvement on Task 6 of SemEval'12. Despite the simple features used, regression models provide good performance, especially for shorter sentences, reaching correlation of 0.62 on the SemEval test set.