Kerz, E.

Qiao, Y.

Ströbel, M.

Wiechmann, D.

English

Over the last years, there has been an increased interest in the combined use of natural language processing techniques and machine learning algorithms to automatically classify texts on the basis of wide range of features. One class of features that have been successfully employed for a wide range of classiﬁcation tasks, including native language identiﬁcation, readability assessment and text genre categorization pertain to the construct of ‘linguistic complexity’. This paper presents a novel approach to the use of linguistic complexity features in text categorization: Rather than representing text complexity ‘globally’ in terms of summary statistics, this approach assesses text complexity ‘locally’ and captures the progression of complexity within a text as a sequence of complexity scores, generating what is referred to here as ‘complexity contours’. We demonstrate the utility of the approach in an automatic text classiﬁcation task for ﬁve genres – academic, newspaper, ﬁction, magazine and spoken – of the Corpus of Contemporary American English (COCA) [Davies, 2008] using a recurrent neural network

International Migration, Integration and Social Cohesion online publications

Text Genre Classification Based on Linguistic Complexity Contours Using A Recurrent Neural Network

https://pure.uva.nl/ws/files/62136767/paper12.pdf

Text Genre Classification Based on Linguistic Complexity Contours Using A Recurrent Neural Network

Abstract

Similar works

Full text

Available Versions

International Migration, Integration and Social Cohesion online publications