  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 important
observations about their cross-lingual consistency and giving suggestions for
more robust evaluation. Last, we explore some geographical and economic factors
that may explain the observed dataset distributions. Code and data are
available here: https://github.com/ffaisal93/dataset_geography. Additional
visualizations are available here: https://nlp.cs.gmu.edu/project/datasetmaps/.

Computation and Language