TY - JOUR
T1 - Native Language Identification with classifier stacking and ensembles
AU - Malmasi, Shervin
AU - Dras, Mark
N1 - 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.
PY - 2018/9
Y1 - 2018/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85053933173&partnerID=8YFLogxK
U2 - 10.1162/COLI_a_00323
DO - 10.1162/COLI_a_00323
M3 - Article
AN - SCOPUS:85053933173
VL - 44
SP - 403
EP - 446
JO - Computational Linguistics
JF - Computational Linguistics
SN - 0891-2017
IS - 3
ER -