Literary detective work on the computer

Literary detective work on the computer

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Bibliographic Details
Main Author: Oakes, Michael P. (Author)
Corporate Author: Ebooks Corporation
Format: Electronic eBook
Language:English
Published: Amsterdam : John Benjamins Publishing Company, 2014.
Subjects:
Computational linguistics > Research.
Imitation in literature.
Plagiarism.
Linguistics > Research > Methodology.
Authorship > Study and teaching.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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245 1 0 |a Literary detective work on the computer  |h [electronic resource] /  |c Michael P. Oakes. 
260 |a Amsterdam :  |b John Benjamins Publishing Company,  |c 2014. 
300 |a 1 online resource. 
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533 |a Electronic reproduction.  |b Perth, W.A.  |n Available via World Wide Web. 
588 |a Description based on print version record. 
505 0 0 |a Machine generated contents note:   |g 1.  |t Introduction --   |g 2.  |t Feature selection --   |g 2.1.  |t Evaluation of feature sets for authorship attribution --   |g 3.  |t Inter-textual distances --   |g 3.1.  |t Manhattan distance and Euclidean distance --   |g 3.2.  |t Labbe and Labbe's measure --   |g 3.3.  |t Chi-squared distance --   |g 3.4.  |t cosine similarity measure --   |g 3.5.  |t Kullback-Leibler Divergence (KLD) --   |g 3.6.  |t Burrows' Delta --   |g 3.7.  |t Evaluation of feature-based measures for inter-textual distance --   |g 3.8.  |t Inter-textual distance by semantic similarity --   |g 3.9.  |t Stemmatology as a measure of inter-textual distance --   |g 4.  |t Clustering techniques --   |g 4.1.  |t Introduction to factor analysis --   |g 4.2.  |t Matrix algebra --   |g 4.3.  |t Use of matrix algebra for PCA --   |g 4.4.  |t PCA case studies --   |g 4.5.  |t Correspondence analysis --   |g 5.  |t Comparisons of classifiers --   |g 6.  |t Other tasks related to authorship --   |g 6.1.  |t $tylochronometry --   |g 6.2.  |t Affect dictionaries and psychological profiling --   |g 6.3.  |t Evaluation of author profiling --   |g 7.  |t Conclusion --   |g 1.  |t Introduction --   |g 2.  |t Plagiarism detection software --   |g 2.1.  |t Collusion and plagiarism, external and intrinsic --   |g 2.2.  |t Preprocessing of corpora and feature extraction --   |g 2.3.  |t Sequence comparison and exact match --   |g 2.4.  |t Source-suspicious document similarity measures --   |g 2.5.  |t Fingerprinting --   |g 2.6.  |t Language models --   |g 2.7.  |t Natural language processing --   |g 2.8.  |t Intrinsic plagiarism detection --   |g 2.9.  |t Plagiarism of program code --   |g 2.10.  |t Distance between translated and original text --   |g 2.11.  |t Direction of plagiarism --   |g 2.12.  |t search engine-based approach used at PAN-13 --   |g 2.13.  |t Case study 1: Hidden influences from printed sources in the Gaelic tales of Duncan and Neil MacDonald --   |g 2.14.  |t Case study 2: General George Pickett and related writings --   |g 2.15.  |t Evaluation methods --   |g 2.16.  |t Conclusion --   |g 3.  |t Spam filters --   |g 3.1.  |t Content-based techniques --   |g 3.2.  |t Building a labeled corpus for training --   |g 3.3.  |t Exact matching techniques --   |g 3.4.  |t Rule-based methods --   |g 3.5.  |t Machine learning --   |g 3.6.  |t Unsupervised machine learning approaches --   |g 3.7.  |t Other spam-filtering problems --   |g 3.8.  |t Evaluation of spam filters --   |g 3.9.  |t Non-linguistic techniques --   |g 3.10.  |t Conclusion --   |g 4.  |t Recommendations for further reading --   |g 1.  |t Introduction --   |g 2.  |t Shakespeare, Wilkins and "Pericles" --   |g 2.1.  |t Correspondence analysis for "Pericles" and related texts --   |g 3.  |t Shakespeare, Fletcher and "The Two Noble Kinsmen" --   |g 4.  |t "King John" --   |g 5.  |t "The Raigne of King Edward III" --   |g 5.1.  |t Neural networks in stylometry --   |g 5.2.  |t Cusum charts in stylometry --   |g 5.3.  |t Burrows' Zeta and Iota --   |g 6.  |t Hand D in "Sir Thomas More" --   |g 6.1.  |t Elliott, Valenza and the Earl of Oxford --   |g 6.2.  |t Elliott and Valenza: Hand D --   |g 6.3.  |t Bayesian approach to questions of Shakespearian authorship --   |g 6.4.  |t Bayesian analysis of Shakespeare's second person pronouns --   |g 6.5.  |t Vocabulary differences, LDA and the authorship of Hand D 13o --   |g 6.6.  |t Hand D: Conclusions --   |g 7.  |t three parts of "Henry VI" --   |g 8.  |t "Timon of Athens" --   |g 9.  |t "The Puritan" and "A Yorkshire Tragedy" --   |g 10.  |t "Arden of Faversham" --   |g 11.  |t Estimation of the extent of Shakespeare's vocabulary and the authorship of the "Taylor" poem --   |g 12.  |t chronology of Shakespeare --   |g 13.  |t Conclusion --   |g 1.  |t Introduction --   |g 1.1.  |t Overview of the New Testament by correspondence analysis --   |g 1.2.  |t Q --   |g 1.3.  |t Luke and Acts --   |g 1.4.  |t Recent approaches to New Testament stylometry --   |g 1.5.  |t Pauline Epistles --   |g 1.6.  |t Hebrews --   |g 1.7.  |t Signs Gospel --   |g 2.  |t Stylometric analysis of the Book of Mormon --   |g 3.  |t Stylometric studies of the Qu'ran --   |g 4.  |t Condupion --   |g 1.  |t Introduction --   |g 1.1.  |t Differences between cryptography and decipherment --   |g 1.2.  |t Cryptological techniques for automatic language recognition --   |g 1.3.  |t Dictionary approaches to language recognition --   |g 1.4.  |t Sinkov's test --   |g 1.5.  |t Index of coincidence --   |g 1.6.  |t log-likelihood ratio --   |g 1.7.  |t chi-squared test statistic --   |g 1.8.  |t Entropy of language --   |g 1.9.  |t Zipf's Law and Heaps' Law coefficients --   |g 1.10.  |t Modal token length --   |g 1.11.  |t Autocorrelation analysis --   |g 1.12.  |t Vowel identification --   |g 2.  |t Rongorongo --   |g 2.1.  |t History of Rongorongo --   |g 2.2.  |t Characteristics of Rongorongo --   |g 2.3.  |t Obstacles to decipherment --   |g 2.4.  |t Encoding of Rongorongo symbols --   |g 2.5.  |t "Mamari" lunar calendar --   |g 2.6.  |t Basic statistics of the Rongorongo corpus --   |g 2.7.  |t Alignment of the Rongorongo corpus --   |g 2.8.  |t concordance for Rongorongo --   |g 2.9.  |t Collocations and collostructions --   |g 2.10.  |t Classification by genre --   |g 2.11.  |t Vocabulary richness --   |g 2.12.  |t Podzniakov's approach to matching frequency curves --   |g 3.  |t Indus Valley texts --   |g 3.1.  |t Why decipherment of the Indus texts is difficult --   |g 3.2.  |t Are the Indus texts writing? --   |g 3.3.  |t Other evidence for the Indus Script being writing --   |g 3.4.  |t Determining the order of the Markov model --   |g 3.5.  |t Missing symbols --   |g 3.6.  |t Text segmentation and the log-likelihood measure --   |g 3.7.  |t Network analysis of the Indus Signs --   |g 4.  |t Linear A --   |g 5.  |t Phaistos disk --   |g 6.  |t Iron Age Pictish symbols --   |g 7.  |t Mayan glyphs --   |g 8.  |t Conclusion. 
650 0 |a Computational linguistics  |x Research. 
650 0 |a Imitation in literature. 
650 0 |a Plagiarism. 
650 0 |a Linguistics  |x Research  |x Methodology. 
650 0 |a Authorship  |x Study and teaching. 
710 2 |a Ebooks Corporation 
776 0 8 |i Print version:  |a Oakes, Michael P.  |t Literary Detective Work on the Computer.  |d John Benjamins Publishing Company 2014  |z 1306705908 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=1682185  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 1 
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919 |a .ulebk  |b 2014-10-15 
915 |a YBP DDA - Also in ProQuest Academic Complete 
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