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020 _a9783030234157
_9978-3-030-23415-7
024 7 _a10.1007/978-3-030-23415-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aForstall, Christopher W.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aQuantitative Intertextuality
_h[electronic resource] :
_bAnalyzing the Markers of Information Reuse /
_cby Christopher W. Forstall, Walter J. Scheirer.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVII, 189 p. 25 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aWhat is Quantitative Intertextuality -- Statistical Learning as a Model for Intertextuality -- Lexical Matching: Text Reuse as Intertextuality -- Semantic Matching: Tracing Reuse by Meaning -- Sound Matching: Capturing Reuse in the Primitive Elements of Language -- Image Matching: Detecting the Reuse of Visual Elements -- Meta-Matching: Combining Evidence From Heterogeneous Sources -- Parting Thoughts.
520 _aThis book introduces quantitative intertextuality, a new approach to the algorithmic study of information reuse in text, sound and images. Employing a variety of tools from machine learning, natural language processing, and computer vision, readers will learn to trace patterns of reuse across diverse sources for scholarly work and practical applications. The respective chapters share highly novel methodological insights in order to guide the reader through the basics of intertextuality. In Part 1, “Theory”, the theoretical aspects of intertextuality are introduced, leading to a discussion of how they can be embodied by quantitative methods. In Part 2, “Practice”, specific quantitative methods are described to establish a set of automated procedures for the practice of quantitative intertextuality. Each chapter in Part 2 begins with a general introduction to a major concept (e.g., lexical matching, sound matching, semantic matching), followed by a case study (e.g., detecting allusions to a popular television show in tweets, quantifying sound reuse in Romantic poetry, identifying influences in fan faction by thematic matching), and finally the development of an algorithm that can be used to reveal parallels in the relevant contexts. Because this book is intended as a “gentle” introduction, the emphasis is often on simple yet effective algorithms for a given matching task. A set of exercises is included at the end of each chapter, giving readers the chance to explore more cutting-edge solutions and novel aspects to the material at hand. Additionally, the book’s companion website includes software (R and C++ library code) and all of the source data for the examples in the book, as well as supplemental content (slides, high-resolution images, additional results) that may prove helpful for exploring the different facets of quantitative intertextuality that are presented in each chapter. Givenits interdisciplinary nature, the book will appeal to a broad audience. From practitioners specializing in forensics to students of cultural studies, readers with diverse backgrounds (e.g., in the social sciences, natural language processing, or computer vision) will find valuable insights.
650 0 _aArtificial intelligence.
650 0 _aInformation storage and retrieval systems.
650 0 _aPattern recognition systems.
650 0 _aSocial sciences
_xData processing.
650 0 _aCulture
_xStudy and teaching.
650 1 4 _aArtificial Intelligence.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aAutomated Pattern Recognition.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aCultural Studies.
700 1 _aScheirer, Walter J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030234133
776 0 8 _iPrinted edition:
_z9783030234140
856 4 0 _uhttps://doi.org/10.1007/978-3-030-23415-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176292
_d176292