Complex Question Answering: Homogeneous or Heterogeneous, Which Ensemble Is Better?
作者： Yllias Chali , Sadid A. Hasan , Mustapha Mojahid
DOI: 10.1007/978-3-319-07983-7_21
关键词: Machine learning 、 Conditional random field 、 Computer science 、 Principle of maximum entropy 、 Hidden Markov model 、 Ensemble learning 、 Artificial intelligence 、 Task (computing) 、 Complex question 、 Support vector machine 、 Base (topology)
摘要: This paper applies homogeneous and heterogeneous ensembles to perform the complex question answering task. For ensemble, we employ Support Vector Machines (SVM) as learning algorithm use a Cross-Validation Committees (CVC) approach form several base models. We SVM, Hidden Markov Models (HMM), Conditional Random Fields (CRF), Maximum Entropy (MaxEnt) techniques build different models for ensemble. Experimental analyses demonstrate that both ensemble methods outperform conventional systems is better.
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sadidhasan.com 参考文章(16)
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