Validity, reliability, and significance: empirical methods for NLP and data science
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael]
Morgan & Claypool Publishers
[2022]
|
| Schriftenreihe: | Synthesis lectures on human language technologies
#55 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvii, 147 Seiten Diagramme |
| ISBN: | 9781636392738 9781636392714 |
Internformat
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Datensatz im Suchindex
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xiii Contents Preface. Acknowledgments 1 2 . xvii Introduction. . . , 1 1.1 Empirical Methods in Machine Learning. 1 1.2 Scope and Outline of this Book. 3 1.3 Intended Readership . . 6 Validity. .9 2.1 Validity Problems in NLP and Data Science . . 2.1.1 Bias Features. 2.1.2 Illegitimate Features. 2.1.3 Circular Features. 2.2 Theories of Measurement and Validity. 12 2.2.1 The Concept of Validity in Psychometrics. 12 2.2.2 The TheoryJ of Scales of Measurement. . 14 2.2.3 Theories of Measurement in Philosophy of Science . 15 Prediction as Measurement. 16 2.3.1 Feature Representations. 17 2.3.2 Measurement Data. .18 2.3 3 xv 9 9 10 11 2.4 Descriptive and Model- Based Validity Tests . . . 19 2.4,1 Dataset Bias Test. . . 20 2.4.2 Transformation
Invariance Test. . . 25 2.4.3 A Model-Based Test for Circularity. 28 2.5 Notes on Practical Usage . . . . 53 Reliability . . 55 3.1 Untangling Terminology: Reliability, Agreement, and Others .55 3.2 Performance Evaluation as Measurement . . 56 3.3 Descriptive and Model-Based Reliability lests . 57 3.3.1 Agreement Coefficients lot Data .Annotation . 57
xiv 3.3.2 3.3.3 3.4 4 Notes on Practical Usage. 88 Significance . 91 4.1 93 4.2 Д Bootstrap Confidence Intervals for Model Evaluation. 61 Model-Based Reliability Testing. 66 Parametric Significance Tests. О Sampling-Based Significance Tests . 4.2.1 Bootstrap Resampling. 4.2.2 Permutation Tests . 97 97 99 4.3 Model-Based Significance Testing . 101 4.3.1 Tie Generalized Likelihood Ratio Test. . . . 102 4.3.2 Likelihood Ratio Tests using LMEMs . 104 4.4 Notes on Practical Usage. 113 Mathematical Background . . 115 A.l Generalized Additive Models. A.1.1 G eneral Form of Model . . A. 1.2 Example. . A. 1.3 Parameter Estimation. . 115 115 116 117 A.2 Linear Mixed Effects Models . A.2.1 General Form of Model. A.2.2 Example . A.2.3 Parameter Optimization. 120 120 121 125 A.3 The Distribution of the Likelihood Ratio
Statistic.126 A.3.1 Score Function and Fisher Information. 126 A.3.2 Taylor Expansion and Asymptotic Distribution. 127 Bibliography. . . 129 Authors’ Biographies . 147 |
| adam_txt |
xiii Contents Preface. Acknowledgments 1 2 . xvii Introduction. . . , 1 1.1 Empirical Methods in Machine Learning. 1 1.2 Scope and Outline of this Book. 3 1.3 Intended Readership . . 6 Validity. .9 2.1 Validity Problems in NLP and Data Science . . 2.1.1 Bias Features. 2.1.2 Illegitimate Features. 2.1.3 Circular Features. 2.2 Theories of Measurement and Validity. 12 2.2.1 The Concept of Validity in Psychometrics. 12 2.2.2 The TheoryJ of Scales of Measurement. . 14 2.2.3 Theories of Measurement in Philosophy of Science . 15 Prediction as Measurement. 16 2.3.1 Feature Representations. 17 2.3.2 Measurement Data. .18 2.3 3 xv 9 9 10 11 2.4 Descriptive and Model- Based Validity Tests . . . 19 2.4,1 Dataset Bias Test. . . 20 2.4.2 Transformation
Invariance Test. . . 25 2.4.3 A Model-Based Test for Circularity. 28 2.5 Notes on Practical Usage . . . . 53 Reliability . . 55 3.1 Untangling Terminology: Reliability, Agreement, and Others .55 3.2 Performance Evaluation as Measurement . . 56 3.3 Descriptive and Model-Based Reliability lests . 57 3.3.1 Agreement Coefficients lot Data .Annotation . 57
xiv 3.3.2 3.3.3 3.4 4 Notes on Practical Usage. 88 Significance . 91 4.1 93 4.2 Д Bootstrap Confidence Intervals for Model Evaluation. 61 Model-Based Reliability Testing. 66 Parametric Significance Tests. О Sampling-Based Significance Tests . 4.2.1 Bootstrap Resampling. 4.2.2 Permutation Tests . 97 97 99 4.3 Model-Based Significance Testing . 101 4.3.1 Tie Generalized Likelihood Ratio Test. . . . 102 4.3.2 Likelihood Ratio Tests using LMEMs . 104 4.4 Notes on Practical Usage. 113 Mathematical Background . . 115 A.l Generalized Additive Models. A.1.1 G eneral Form of Model . . A. 1.2 Example. . A. 1.3 Parameter Estimation. . 115 115 116 117 A.2 Linear Mixed Effects Models . A.2.1 General Form of Model. A.2.2 Example . A.2.3 Parameter Optimization. 120 120 121 125 A.3 The Distribution of the Likelihood Ratio
Statistic.126 A.3.1 Score Function and Fisher Information. 126 A.3.2 Taylor Expansion and Asymptotic Distribution. 127 Bibliography. . . 129 Authors’ Biographies . 147 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:54:31Z |
| indexdate | 2024-07-20T05:28:00Z |
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| isbn | 9781636392738 9781636392714 |
| language | English |
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| spelling | Riezler, Stefan Verfasser (DE-588)1033925454 aut Validity, reliability, and significance empirical methods for NLP and data science Stefan Riezler, Michael Hagmann [San Rafael] Morgan & Claypool Publishers [2022] © 2022 xvii, 147 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Synthesis lectures on human language technologies #55 Linguistics Automatische Sprachanalyse (DE-588)4129935-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Automatische Sprachanalyse (DE-588)4129935-8 s Data Mining (DE-588)4428654-5 s DE-604 Hagmann, Michael 1981- Verfasser (DE-588)1193940567 aut Erscheint auch als Online-Ausgabe, PDF 978-1-63639-272-1 Synthesis lectures on human language technologies #55 (DE-604)BV035447238 55 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033053114&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Riezler, Stefan Hagmann, Michael 1981- Validity, reliability, and significance empirical methods for NLP and data science Synthesis lectures on human language technologies Linguistics Automatische Sprachanalyse (DE-588)4129935-8 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4129935-8 (DE-588)4428654-5 |
| title | Validity, reliability, and significance empirical methods for NLP and data science |
| title_auth | Validity, reliability, and significance empirical methods for NLP and data science |
| title_exact_search | Validity, reliability, and significance empirical methods for NLP and data science |
| title_exact_search_txtP | Validity, reliability, and significance empirical methods for NLP and data science |
| title_full | Validity, reliability, and significance empirical methods for NLP and data science Stefan Riezler, Michael Hagmann |
| title_fullStr | Validity, reliability, and significance empirical methods for NLP and data science Stefan Riezler, Michael Hagmann |
| title_full_unstemmed | Validity, reliability, and significance empirical methods for NLP and data science Stefan Riezler, Michael Hagmann |
| title_short | Validity, reliability, and significance |
| title_sort | validity reliability and significance empirical methods for nlp and data science |
| title_sub | empirical methods for NLP and data science |
| topic | Linguistics Automatische Sprachanalyse (DE-588)4129935-8 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Linguistics Automatische Sprachanalyse Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033053114&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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