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Count-based and predictive language models for exploring DeReKo

  • We present the use of count-based and predictive language models for exploring language use in the German Reference Corpus DeReKo. For collocation analysis along the syntagmatic axis we employ traditional association measures based on co-occurrence counts as well as predictive association measures derived from the output weights of skipgram word embeddings. For inspecting the semantic neighbourhood of words along the paradigmatic axis we visualize the high dimensional word embeddings in two dimensions using t-stochastic neighbourhood embeddings. Together, these visualizations provide a complementary, explorative approach to analysing very large corpora in addition to corpus querying. Moreover, we discuss count-based and predictive models w.r.t. scalability and maintainability in very large corpora.

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Author:Peter FankhauserGND, Marc KupietzORCiDGND
Parent Title (English):Proceedings of the LREC 2022 Workshop on Challenges in the Management of Large Corpora (CMLC-10 2022). Marseille, 20 June 2022
Publisher:European Language Resources Association (ELRA)
Place of publication:Paris
Editor:Piotr Bański, Adrien Barbaresi, Simon Clematide, Marc Kupietz, Harald Lüngen
Document Type:Conference Proceeding
Year of first Publication:2022
Date of Publication (online):2022/07/01
Publishing Institution:Leibniz-Institut für Deutsche Sprache (IDS)
Tag:Deutsches Referenzkorpus (DeReKo)
German Reference Corpus (DeReKo); collocation analysis; language models; word embeddings
GND Keyword:Assoziationsmaß; Deutsch; Kollokation; Korpus <Linguistik>; Paradigma; Syntagma
First Page:27
Last Page:31
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Leibniz-Classification:Sprache, Linguistik
Program areas:S1: Korpuslinguistik
Program areas:S2: Forschungskoordination und –infrastrukturen
Licence (English):License LogoCreative Commons - Attribution-NonCommercial 4.0 International