Conference article

Comparing the Performance of Feature Representations for the Categorization of the Easy-to-Read Variety vs Standard Language

Marina Santini
RISE Research Institutes of Sweden, (Division ICT - RISE SICS East), Stockholm, Sweden

Benjamin Danielsson
Department of Computer and Information Science , Linköping University, Linköping, Sweden

Arne Jönsson
RISE Research Institutes of Sweden, Stockholm, Sweden / Department of Computer and Information Science , Linköping University, Linköping, Sweden

Download article

Published in: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland

Linköping Electronic Conference Proceedings 167:11, p. 105--114

NEALT Proceedings Series 42:11, p. 105--114

Show more +

Published: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (print), 1650-3740 (online)


We explore the effectiveness of four feature representations -- bag-of-words, word embeddings, principal components and autoencoders -- for the binary categorization of the easy-to-read variety vs standard language. Standard language refers to the ordinary language variety used by a population as a whole or by a community, while the ``easy-to-read’’ variety is a simpler (or a simplified) version of the standard language. We test the efficiency of these feature representations on three corpora, which differ in size, class balance, unit of analysis, language and topic. We rely on supervised and unsupervised machine learning algorithms. Results show that bag-of-words is a robust and straightforward feature representation for this task and performs well in many experimental settings. Its performance is equivalent or equal to the performance achieved with principal components and autoencorders, whose preprocessing is however more time-consuming. Word embeddings are less accurate than the other feature representations for this classification task.


feature representation text classification easy-to-read variety standard language weka supervised machine learning deep learning clustering bag-of-words principal components autoencoders word embeddings


No references available

Citations in Crossref