This paper applies homogeneous and heterogeneous ensembles to perform the complex question answering task. For the homogeneous ensemble, we employ Support Vector Machines (SVM) as the learning algorithm and use a Cross-Validation Committees (CVC) approach to form several base models. We use SVM, Hidden Markov Models (HMM), Conditional Random Fields (CRF), and Maximum Entropy (MaxEnt) techniques to build different base models for the heterogeneous ensemble. Experimental analyses demonstrate that both ensemble methods outperform conventional systems and heterogeneous ensemble is better. © Springer International Publishing Switzerland 2014.
CITATION STYLE
Chali, Y., Hasan, S. A., & Mojahid, M. (2014). Complex question answering: Homogeneous or heterogeneous, which ensemble is better? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8455 LNCS, pp. 160–163). Springer Verlag. https://doi.org/10.1007/978-3-319-07983-7_21
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