Native Language Identification with classifier stacking and ensembles

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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.

Original languageEnglish
Pages (from-to)403-446
Number of pages44
JournalComputational Linguistics
Volume44
Issue number3
DOIs
Publication statusPublished - Sep 2018

Bibliographical note

Copyright the Association for Computational Linguistics 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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