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Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science (Synthesis Lectures on Human Language Technologies) - Stefan Riezler June 1, 2022 PDF  BOOKS
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Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science (Synthesis Lectures on Human Language Technologies)
Author: Stefan Riezler
Year: June 1, 2022
Format: PDF
File size: PDF 17 MB
Language: English



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Book Description: Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science Stefan Riezler June 1, 2022 Pages: Genre: Non-Fiction, Technology, Machine Learning, Data Science Synopsis: In this book, [Insert Author's Name] delves into the methodological questions of empirical sciences, specifically in the fields of natural language processing (NLP) and data science, addressing issues of validity, reliability, and significance through statistical techniques. The author focuses on model-based empirical methods that utilize interpretable probabilistic models from well-understood families such as generalized additive models (GAMs) and linear mixed effects models (LMEMs). These models are used to assess the validity, reliability, and significance of data annotation and machine learning predictions. The book begins by discussing the need to study and understand the technological process of developing modern knowledge, emphasizing the importance of developing a personal paradigm for perceiving the technological evolution of humanity. This paradigm is crucial for the survival of humanity and the unification of people in a warring state. The author argues that understanding these concepts is essential for the future of humanity and the ability to adapt to new technologies. The book is divided into three main sections, each focusing on a specific aspect of empirical methods. The first section covers the validity test, which detects circular features that circumvent learning.
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有効性、信頼性、意義:NLPおよびデータサイエンスの実証的方法Stefan Riezler June 1、2022 Pages: ジャンル:Non-Fiction、 Technology、 Machine arning、 Data Science Synopsis:本書[Insert Author's Name]は、特に自然言語処理(NLP)やデータサイエンスにおける実証科学の方法論的問題を掘り下げ、有効性、信頼性、有意性の問題を統計的手法を用いている。著者は、Generalized Additive Models (GAM)やLinear Mixed Effects Models (LMEM)など、よく理解されたファミリーから解釈可能な確率モデルを使用するモデルベースの経験的手法に焦点を当てています。これらのモデルは、データ注釈と機械学習予測の有効性、信頼性、および重要性を評価するために使用されます。本書は、人類の技術進化の認識のための個人的なパラダイムを開発することの重要性を強調し、近代的な知識の開発の技術的プロセスを研究し、理解する必要性の議論から始まります。このパラダイムは、人類の存続と戦争状態における人々の統一のために不可欠です。これらの概念を理解することは、人類の未来と新しい技術に適応する能力にとって不可欠であると著者は論じている。本は3つの主要なセクションに分かれており、それぞれが経験的な方法の特定の側面に焦点を当てています。最初のセクションでは、学習を迂回する円形の特徴を検出する有効性テストについて説明します。

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