<div class="csl-bib-body">
<div class="csl-entry">Baumann, A., Hofmann, K., Marakasova, A., Neidhardt, J., & Wissik, T. (2023). Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach. <i>Cognitive Linguistics</i>, <i>34</i>(3–4), 533–568. https://doi.org/10.1515/cog-2022-0008</div>
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dc.identifier.issn
0936-5907
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/191531
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dc.description.abstract
This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word's degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form-function pairings between words and their habitual usage contexts.
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dc.description.sponsorship
Österr. Akademie der Wissenschaften
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dc.language.iso
en
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dc.publisher
De Gruyter
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dc.relation.ispartof
Cognitive Linguistics
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
corpus linguistics
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dc.subject
diachronic linguistics
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dc.subject
German
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dc.subject
semantic networks
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dc.subject
semantics
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dc.title
Semantic micro-dynamics as a reflex of occurrence frequency: a semantic networks approach