| 书目名称 | Statistical Significance Testing for Natural Language Processing | | 编辑 | Rotem Dror,Lotem Peled-Cohen,Roi Reichart | | 视频video | | | 丛书名称 | Synthesis Lectures on Human Language Technologies | | 图书封面 |  | | 描述 | .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 drivesthe 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 | | 出版日期 | Book 2020 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02174-9 | | isbn_softcover | 978-3-031-01046-0 | | isbn_ebook | 978-3-031-02174-9Series ISSN 1947-4040 Series E-ISSN 1947-4059 | | issn_series | 1947-4040 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 16:19:18
