Abstract
The field of Natural Language Processing (NLP) has made substantial progress in the last two decades. This progress stems from multiple reasons: the data revolution that has made abundant amounts of textual data from a variety of languages and linguistic domains available, the development of increasingly effective predictive statistical models, and the availability of hardware that can apply these models to large datasets. This dramatic improvement in the capabilities of NLP algorithms carry the potential for a great impact.
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Dror, R., Peled-Cohen, L., Shlomov, S., Reichart, R. (2020). Introduction. In: Statistical Significance Testing for Natural Language Processing. Synthesis Lectures on Human Language Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-02174-9_1
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DOI: https://doi.org/10.1007/978-3-031-02174-9_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-01046-0
Online ISBN: 978-3-031-02174-9
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