Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity systems

Abhay L. Kashyap, Lushan Han, Roberto Yus, Jennifer Sleeman, Taneeya W. Satyapanich, Sunil R Gandhi, and Tim Finin

August 23, 2014

We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TF-IDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-of-words approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems.

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Tags: ai, human language technology, lexical semantics, natural language processing, natural language processing, semantic similarity, semantics

Type: InProceedings

Publisher: Association for Computational Linguistics

Note: DOI: 10.3115/v1/S14-2072

Downloads: 4578 downloads