Statistical Significance Testing for Natural Language Processing

Statistical Significance Testing for Natural Language Processing
Author: Rotem Dror
Publisher: Morgan & Claypool Publishers
Total Pages: 118
Release: 2020-04-03
Genre: Computers
ISBN: 1681737965

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Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.


Statistical Significance Testing for Natural Language Processing
Language: en
Pages: 118
Authors: Rotem Dror
Categories: Computers
Type: BOOK - Published: 2020-04-03 - Publisher: Morgan & Claypool Publishers

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Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has becom
On the Statistical Significance Testing for Natural Language Processing
Language: en
Pages: 127
Authors: Haotian Zhu
Categories:
Type: BOOK - Published: 2020 - Publisher:

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This thesis explores and compares statistical significance tests frequently used in comparing Natural Language Processing (NLP) system performance in several as
Validity, Reliability, and Significance
Language: en
Pages: 147
Authors: Stefan Riezler
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

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Empirical methods are means to answering methodological questions of empirical sciences by statistical techniques. The methodological questions addressed in thi
Statistical Significance Testing for Natural Language Processing
Language: en
Pages: 98
Authors: Rotem Dror
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

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Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has becom
Introduction to Natural Language Processing
Language: en
Pages: 535
Authors: Jacob Eisenstein
Categories: Computers
Type: BOOK - Published: 2019-10-01 - Publisher: MIT Press

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A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algo