000 04295nam a22006255i 4500
001 978-3-031-57065-0
003 DE-He213
005 20251030112305.0
007 cr nn 008mamaa
008 240609s2024 sz | s |||| 0|eng d
020 _a9783031570650
_9978-3-031-57065-0
024 7 _a10.1007/978-3-031-57065-0
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
100 1 _aRiezler, Stefan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978491
245 1 0 _aValidity, Reliability, and Significance
_h[electronic resource] :
_bEmpirical Methods for NLP and Data Science /
_cby Stefan Riezler, Michael Hagmann.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVII, 168 p. 70 illus., 61 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface -- Acknowledgments -- Introduction -- Validity -- Reliability -- Significance -- Worked-Through Example: Analyzing Inferential Reproducibility -- Bibliography.
520 _aThis book introduces empirical methods for machine learning with a special focus on applications in natural language processing (NLP) and data science. The authors present problems of validity, reliability, and significance and provide common solutions based on statistical methodology to solve them. The book focuses on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows for the detection of circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Lastly, a significance test based on the likelihood ratios of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. The book is self-contained with an appendix on the mathematical background of generalized additive models and linear mixed effects models as well as an accompanying webpage with the related R and Python code to replicate the presented experiments. The second edition also features a new hands-on chapter that illustrates how to use the included tools in practical applications.
650 0 _aArtificial intelligence.
_958441
650 0 _aMachine learning.
_955059
650 0 _aComputer science
_xMathematics.
_964041
650 0 _aMathematical statistics.
_967233
650 0 _aExperimental design.
_964006
650 0 _aNatural language processing (Computer science).
_965131
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aArtificial Intelligence.
_958441
650 2 4 _aMachine Learning.
_955059
650 2 4 _aProbability and Statistics in Computer Science.
_967234
650 2 4 _aDesign of Experiments.
_974873
650 2 4 _aNatural Language Processing (NLP).
_966164
650 2 4 _aData Science.
700 1 _aHagmann, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978492
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031570643
776 0 8 _iPrinted edition:
_z9783031570667
776 0 8 _iPrinted edition:
_z9783031570674
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_966859
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57065-0
912 _aZDB-2-SXSC
999 _c60627
_d60627