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Investigating the importance of linguistic complexity features across different datasets related to language learning.

Conference paper
Authors Ildikó Pilán
Elena Volodina
Published in Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing, COLING, Santa Fe, New Mexico, USA, August 25, 2018.
ISBN 978-1-948087-62-9
Publisher Association of Computational Linguistics
Publication year 2018
Published at Department of Swedish
Language en
Links aclweb.org/anthology/W18-4606
Keywords readability of (second language learner) texts, automatic prediction of complexity levels, machine learning, predictive features
Subject categories Language Technology (Computational Linguistics), Specific Languages, General Language Studies and Linguistics

Abstract

We present the results of our investigations aiming at identifying the most informative linguistic complexity features for classifying language learning levels in three different datasets. The datasets vary across two dimensions: the size of the instances (texts vs. sentences) and the language learning skill they involve (reading comprehension texts vs. texts written by learners themselves). We present a subset of the most predictive features for each dataset, taking into consid- eration significant differences in their per-class mean values and show that these subsets lead not only to simpler models, but also to an improved classification performance. Furthermore, we pin-point fourteen central features that are good predictors regardless of the size of the linguistic unit analyzed or the skills involved, which include both morpho-syntactic and lexical dimensions.

Page Manager: Webmaster|Last update: 9/11/2012
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