@inproceedings{pilan-etal-2016-coursebook,
title = "Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners{'} Writings",
author = "Pil{\'a}n, Ildik{\'o} and
Alfter, David and
Volodina, Elena",
editor = "Brunato, Dominique and
Dell{'}Orletta, Felice and
Venturi, Giulia and
Fran{\c{c}}ois, Thomas and
Blache, Philippe",
booktitle = "Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity ({CL}4{LC})",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4114",
pages = "120--126",
abstract = "We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreign and second language learning context can be expressed in terms of proficiency levels. We show that incorporating features capturing lexical complexity information from reading passages can boost significantly the machine learning based classification of learner-written texts into proficiency levels. With an F1 score of .8 our system rivals state-of-the-art results reported for other languages for this task. Finally, we present a freely available web-based tool for proficiency level classification and lexical complexity visualization for both learner writings and reading texts.",
}
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<abstract>We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreign and second language learning context can be expressed in terms of proficiency levels. We show that incorporating features capturing lexical complexity information from reading passages can boost significantly the machine learning based classification of learner-written texts into proficiency levels. With an F1 score of .8 our system rivals state-of-the-art results reported for other languages for this task. Finally, we present a freely available web-based tool for proficiency level classification and lexical complexity visualization for both learner writings and reading texts.</abstract>
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%0 Conference Proceedings
%T Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners’ Writings
%A Pilán, Ildikó
%A Alfter, David
%A Volodina, Elena
%Y Brunato, Dominique
%Y Dell’Orletta, Felice
%Y Venturi, Giulia
%Y François, Thomas
%Y Blache, Philippe
%S Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F pilan-etal-2016-coursebook
%X We bring together knowledge from two different types of language learning data, texts learners read and texts they write, to improve linguistic complexity classification in the latter. Linguistic complexity in the foreign and second language learning context can be expressed in terms of proficiency levels. We show that incorporating features capturing lexical complexity information from reading passages can boost significantly the machine learning based classification of learner-written texts into proficiency levels. With an F1 score of .8 our system rivals state-of-the-art results reported for other languages for this task. Finally, we present a freely available web-based tool for proficiency level classification and lexical complexity visualization for both learner writings and reading texts.
%U https://aclanthology.org/W16-4114
%P 120-126
Markdown (Informal)
[Coursebook Texts as a Helping Hand for Classifying Linguistic Complexity in Language Learners’ Writings](https://aclanthology.org/W16-4114) (Pilán et al., CL4LC 2016)
ACL