@article{malmasi-dras-2018-native,
title = "Native Language Identification With Classifier Stacking and Ensembles",
author = "Malmasi, Shervin and
Dras, Mark",
journal = "Computational Linguistics",
volume = "44",
number = "3",
month = sep,
year = "2018",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/J18-3003",
doi = "10.1162/coli_a_00323",
pages = "403--446",
abstract = "Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.",
}
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<abstract>Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.</abstract>
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%0 Journal Article
%T Native Language Identification With Classifier Stacking and Ensembles
%A Malmasi, Shervin
%A Dras, Mark
%J Computational Linguistics
%D 2018
%8 sep
%V 44
%N 3
%I MIT Press
%C Cambridge, MA
%F malmasi-dras-2018-native
%X Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.
%9 journal article
%R 10.1162/coli_a_00323
%U https://aclanthology.org/J18-3003
%U https://doi.org/10.1162/coli_a_00323
%P 403-446
Markdown (Informal)
[Native Language Identification With Classifier Stacking and Ensembles](https://aclanthology.org/J18-3003) (Malmasi & Dras, CL 2018)
ACL