Quantitative intertextuality: analyzing the markers of information reuse
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer
[2019]
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031448045&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031448045&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xvii, 189 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030234133 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Parti The Theory 1 What is QuantitativeIntertextuality?........................................................ 1.1 Introduction.......................................................................................... 1.2 Quantitative approaches....................................................................... 1.3 Authorship attribution and stylometry ............................................... 1.4 Text reuse.............................................................................................. 1.5 Synthesis of theory and practice......................................................... References...................................................................................................... 3 3 6 9 13 18 19 2 Statistical Learning as a Modelfor Intertextuality................................. 2.1 Theory and practice: a continuing relationship.................................. 2.2 Origins of intertextual theory............................................................... 2.3 Large-scale effects................................................................................. 2.4 Local effects.......................................................................................... 2.5 Other divisions....................................................................................... 2.6 A bottom-up model............................................................................... 2.7 The act of recognition........................................................................... 2.8 Intertextual origins — oral
theory....................................................... 2.9 Expertise and pattern recognition ....................................................... 2.10 A general model for intertextuality..................................................... 2.11 The subjective experience of intertext................................................. 2.12 Where does intertext happen?............................................................. 2.13 Intertextuality and interpretive communities...................................... 2.14 The presence of the unsaid................................................................... 2.15 Machine learning and intertext ........................................................... References...................................................................................................... 23 23 26 28 29 31 32 33 35 37 38 40 41 43 45 47 49 xiii
xjv Contents Part II The Practice 3 Lexical Matching: Text Reuse as Intertextuality.................................... 3.1 Words: The Basic Unit of Intertext...................................................... 3.2 Search Procedures.................................................................................. 3.3 Case Study: “Game of Thrones” and its Audience.............................. 3.4 Words as Features for Allusion Detection ........................................... 3.5 Additional Word-based Features........................................................... 3.6 Discussion.............................................................................................. References...................................................................................................... 55 55 57 58 62 69 74 75 4 Semantic Matching: Tracing Reuse by Meaning..................................... 79 4.1 The Meaning of a Text as an Intertextual Marker............................... 79 4.2 Case Study: Fiction from the Internet Writing Community Wattpad. 83 4.3 The Latent Semantic Indexing Approach............................................. 87 4.4 Computing Semantic Similarity............................................................. 90 4.5 Applications of Semantic Matching..................................................... 94 4.6 Discussion.............................................................................................. 99 4.7 Exercises ................................................................................................ 100
References........................................................................................................ 102 5 Sound Matching: Capturing Reuse in the Primitive Elements of Language ....................................................................................................... 105 5.1 Repetitive Sound as a Style Marker.......................................................105 5.2 Case study: Cowper’s Influence on Wordsworth..................................109 5.3 The Functional ո-gram Feature.............................................................Ill 5.4 Computing Relative Distance in the Feature Space of Sound............ 116 5.5 Open Set Influence Detection.................................................................120 5.6 A One-Class Methodology for Learning............................................... 121 5.7 Discussion..............................................................................................125 5.8 Exercises ................................................................................................127 References........................................................................................................128 6 Image Matching: Detecting the Reuse of Visual Elements......................133 6.1 What is Style in a Visual Sense?...........................................................133 6.2 Case study: Three Popular Memes .......................................................136 6.3 Histogram of Oriented Gradients...........................................................140 6.4 Nearest Neighbor
Classification to Detect Reuse..................................143 6.5 Detecting Reuse in Meme Images and Rejecting Irrelevant Samples 146 6.6 Discussion..............................................................................................149 6.7 Exercises ................................................................................................151 References....................................................................................................... 152 7 Mcta-Matching: Combining Evidence From Heterogeneous Sources . 155 7.1 Combining Information in a Sensible Way...........................................155 7.2 Case study: The Self-Plagiarism of JonahLehrer................................ 158
Contents xv 7.3 Feature-level Fusion.............................................................................161 7.4 A Better Tool: W-SVM......................................................................... 167 7.5 Combining Hvidcnce Related to the Case Study..................................172 7.6 Discussion.............................................................................................175 7.7 Hxercises .............................................................................................. 176 References...................................................................................................... 177 8 Parting Thoughts ................................................................................. 181 8.1 What Have We Learned? ..................................................................... 181 8.2 Logical Next Steps l or Quantitative Intertextuality............................. 182 8.3 Building a Relationship with Psychology............................................ 185 References...................................................................................................... 187
Ibis 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. lhe 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. Given its 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.
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| id | DE-604.BV046066808 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:42:18Z |
| institution | BVB |
| isbn | 9783030234133 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031448045 |
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| physical | xvii, 189 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
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| spellingShingle | Forstall, Christopher W. ca. 20./21. Jahrhundert Scheirer, Walter J. ca. 20./21. Jahrhundert Quantitative intertextuality analyzing the markers of information reuse Information Retrieval (DE-588)4072803-1 gnd Intertextualität (DE-588)4114051-5 gnd Intermedialität (DE-588)4605581-2 gnd |
| subject_GND | (DE-588)4072803-1 (DE-588)4114051-5 (DE-588)4605581-2 |
| title | Quantitative intertextuality analyzing the markers of information reuse |
| title_auth | Quantitative intertextuality analyzing the markers of information reuse |
| title_exact_search | Quantitative intertextuality analyzing the markers of information reuse |
| title_full | Quantitative intertextuality analyzing the markers of information reuse Christopher W. Forstall, Walter J. Scheirer |
| title_fullStr | Quantitative intertextuality analyzing the markers of information reuse Christopher W. Forstall, Walter J. Scheirer |
| title_full_unstemmed | Quantitative intertextuality analyzing the markers of information reuse Christopher W. Forstall, Walter J. Scheirer |
| title_short | Quantitative intertextuality |
| title_sort | quantitative intertextuality analyzing the markers of information reuse |
| title_sub | analyzing the markers of information reuse |
| topic | Information Retrieval (DE-588)4072803-1 gnd Intertextualität (DE-588)4114051-5 gnd Intermedialität (DE-588)4605581-2 gnd |
| topic_facet | Information Retrieval Intertextualität Intermedialität |
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