Abstract
In this paper, we explore whether there exist ‘contrast’ features that help recognize if a text variety is a genre or a domain. We carry out our experiments on the text varieties that are included in the Swedish national corpus, called Stockholm-Umeå Corpus or SUC, and build several text classification models based on text complexity features, grammatical features, bag-of-words features and word embeddings. Results show that text complexity features and grammatical features systematically perform better on genres rather than on domains. This indicates that these features can be used as ‘contrast’ features because, when in doubt about the nature of a text category, they help bring it to light.
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Weighted Averaged F-Measure is the sum of all the classes F-measures, each weighted according to the number of instances with that particular class label. It is a more reliable metric than the harmonic F-measures (F1).
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Acknowledgements
This research was supported by E-care@home, a “SIDUS – Strong Distributed Research Environment” project, funded by the Swedish Knowledge Foundation [kk-stiftelsen, Diarienr: 20140217]. Project website: http://ecareathome.se/
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Santini, M., Danielsson, B., Jönsson, A. (2019). Introducing the Notion of ‘Contrast’ Features for Language Technology. In: Anderst-Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2019. Communications in Computer and Information Science, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-27684-3_24
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