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Dependency Lengths in Speech and Writing: A Cross-Linguistic Comparison via YouDePP, a Pipeline for Scraping and Parsing YouTube Captions ...
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Dependency Lengths in Speech and Writing: A Cross-Linguistic Comparison via YouDePP, a Pipeline for Scraping and Parsing YouTube Captions
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Abstract:
Recording, transcribing, and annotating naturalistic spoken data is typically difficult and time-intensive. Online sources, however, are a rich and relatively untapped source of naturalistic speech. Using corpora of 7 languages gathered via YouDePP, a pipeline for scraping and dependency-parsing pre-transcribed speech from YouTube, I investigate how dependency length minimization (DLM) varies across written and spoken modalities. I compare the dependency length growth rates in these corpora to those in Universal Dependencies 2.6 and find that dependency lengths in writing are not consistently longer than those in speech. Rather, the dependency lengths of more head-initial, SVO languages grew at a slightly faster rate in speech than in writing, while the reverse pattern held for more head-final, SOV languages.
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Keyword:
Computational Linguistics; dependency length minimization; dependency locality; spoken corpora; Typological Linguistics and Linguistic Diversity; typology; YouTube
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URL: https://scholarworks.umass.edu/scil/vol4/iss1/37
https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1175&context=scil
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A Data-driven Approach to Crosslinguistic Structural Biases
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In: Proceedings of the Society for Computation in Linguistics (2021)
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