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Dataset Geography: Mapping Language Data to Language Users. (arXiv:2112.03497v2 [cs.CL] UPDATED)
March 28, 2022, 1:11 a.m. | Fahim Faisal, Yinkai Wang, Antonios Anastasopoulos
cs.CL updates on arXiv.org arxiv.org
As language technologies become more ubiquitous, there are increasing efforts
towards expanding the language diversity and coverage of natural language
processing (NLP) systems. Arguably, the most important factor influencing the
quality of modern NLP systems is data availability. In this work, we study the
geographical representativeness of NLP datasets, aiming to quantify if and by
how much do NLP datasets match the expected needs of the language speakers. In
doing so, we use entity recognition and linking systems, also making …
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