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Conference Papers Year : 2014

Complex question answering: homogeneous or heterogeneous, which ensemble is better?

Abstract

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.
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Dates and versions

hal-01402563 , version 1 (24-11-2016)

Identifiers

  • HAL Id : hal-01402563 , version 1
  • OATAO : 15167

Cite

Yllias Chali, Hassan Sadid, Mustapha Mojahid. Complex question answering: homogeneous or heterogeneous, which ensemble is better?. 19th International Conference on Application of Natural Language to Information Systems (NLDB 2014), Jun 2014, Montpellier, France. pp. 160-163. ⟨hal-01402563⟩
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