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Conference object . 2021
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Part of book or chapter of book . 2014
License: CC BY NC SA
Data sources: KNAW Pure
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Applying automatically parsed corpora to the study of language variation.

Authors: Bloem, J.; Versloot, A.; Weerman, F.;

Applying automatically parsed corpora to the study of language variation.

Abstract

In this work, we discuss the benefits of using automatically parsed corpora to study language variation. The study of language variation is an area of linguistics in which quantitative methods have been particularly successful. We argue that the large datasets that can be obtained using automatic annotation can help drive further research in this direction, providing sufficient data for the increasingly complex models used to describe variation. We demonstrate this by replicating and extending a previous quantitative variation study that used manually and semi-automatically annotated data.We show that while the study cannot be replicated completely due to limitations of the existing automatic annotation, we can draw at least the same conclusions as the original study. In addition, we demonstrate the flexibility of this method by extending the findings to related linguistic constructions and to another domain of text, using additional data.

Country
Netherlands
Keywords

410, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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