An investigation into the application of ensemble learning for entailment classification

Authors:
Niall Rooney;Hui Wang;Philip S. Taylor
Affiliations:
Artificial Intelligence and Applications Group, School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Newtownabbey BT37 OQB, United Kingdom;Artificial Intelligence and Applications Group, School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Newtownabbey BT37 OQB, United Kingdom;SAP(UK) Ltd., The Concourse, Queen's Road, Queen's Island, Titanic Quarter, Belfast BT3 9DT, United Kingdom
Venue:
Information Processing and Management: an International Journal
Year:
2014

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Abstract

Textual entailment is a task for which the application of supervised learning mechanisms has received considerable attention as driven by successive Recognizing Data Entailment data challenges. We developed a linguistic analysis framework in which a number of similarity/dissimilarity features are extracted for each entailment pair in a data set and various classifier methods are evaluated based on the instance data derived from the extracted features. The focus of the paper is to compare and contrast the performance of single and ensemble based learning algorithms for a number of data sets. We showed that there is some benefit to the use of ensemble approaches but, based on the extracted features, Naive Bayes proved to be the strongest learning mechanism. Only one ensemble approach demonstrated a slight improvement over the technique of Naive Bayes.