| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910826746603321 |
|
|
Autore |
Oakes Michael P. |
|
|
Titolo |
Literary detective work on the computer / / Michael P. Oakes |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 |
|
©2014 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (293 p.) |
|
|
|
|
|
|
Collana |
|
Natural Language Processing ; ; Volume 12 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Computational linguistics - Research |
Imitation in literature |
Plagiarism |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Description based upon print version of record. |
|
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Literary Detective Work on the Computer; Editorial page; Title page; LCC data; Table of contents; Preface; 1. Author identification; 1. Introduction; 2. Feature selection; 2.1 Evaluation of feature sets for authorship attribution; 3. Inter-textual distances; 3.1 Manhattan distance and Euclidean distance; 3.2 Labbé and Labbé's measure; 3.3 Chi-squared distance; 3.4 The cosine similarity measure; 3.6 Burrows' Delta; 3.5 Kullback-Leibler Divergence (KLD); 3.7 Evaluation of feature-based measures for inter-textual distance; 3.8 Inter-textual distance by semantic similarity |
3.9 Stemmatology as a measure of inter-textual distance4. Clustering techniques; 4.1 Introduction to factor analysis; 4.2 Matrix algebra; 4.3 Use of matrix algebra for PCA; 4.4 PCA case studies; 4.5 Correspondence analysis; 5. Comparisons of classifiers; 6. Other tasks related to authorship; 6.1 Stylochronometry; 6.2 Affect dictionaries and psychological profiling; 6.3 Evaluation of author profiling; 7. Conclusion; 2. Plagiarism and spam filtering; 1. Introduction; 2. Plagiarism detection software; 2.1 Collusion and plagiarism, external and intrinsic |
2.2 Preprocessing of corpora and feature extraction2.3 Sequence comparison and exact match; 2.4 Source-suspicious document similarity measures; 2.5 Fingerprinting; 2.6 Language models; 2.7 |
|
|
|
|
|
|
|
|
|
|
|
Natural Language Processing; 2.8 Intrinsic plagiarism detection; 2.9 Plagiarism of program code; 2.10 Distance between translated and original text; 2.11 Direction of plagiarism; 2.12 The search engine-based approach used at PAN-13; 2.13 Case study 1: Hidden influences from printed sources in the Gaelic tales; 2.14 Case study 2: General George Pickett and related writings; 2.15 Evaluation methods |
2.16 Conclusion3. Spam filters; 3.1 Content-based techniques; 3.2 Building a labelled corpus for training; 3.3 Exact matching techniques; 3.4 Rule-based methods; 3.5 Machine learning; 3.5.1 Naïve Bayes; 3.5.2 Logistic regression; 3.5.3 Boosting; 3.6 Unsupervised machine learning approaches; 3.7 Other spam-filtering problems; 3.8 Evaluation of spam filters; 3.9 Non-linguistic techniques; 3.9.1 Safelists; 3.9.2 Human challenges; 3.9.3 Reputation analysis; 3.9.4 Networking considerations; 3.9.5 Web harvesting; 3.9.6 Payment and legislation; 3.10 Conclusion; 4. Recommendations for further reading |
3. Computer studies of Shakespearean authorship1. Introduction; 2. Shakespeare, Wilkins and Pericles; 2.1 Correspondence analysis for ""Pericles"" and related texts; 3. Shakespeare, Fletcher and The Two Noble Kinsmen; 4. King John; 5. The Raigne of King Edward III; 5.1 Neural networks in stylometry; 5.2 Cusum charts in stylometry; 5.3 Burrows' Zeta and Iota; 6. Hand D in "Sir Thomas More"; 6.1 Elliott, Valenza and the Earl of Oxford; 6.2 Elliott and Valenza: Hand D; 6.3 Bayesian approach to questions of Shakespearian authorship; 6.4 Bayesian analysis of Shakespeare's second-person pronouns |
6.5 Vocabulary differences, LDA and the authorship of Hand D |
|
|
|
|
|
|
Sommario/riassunto |
|
Computational linguistics can be used to uncover mysteries in text which are not always obvious to visual inspection. For example, the computer analysis of writing style can show who might be the true author of a text in cases of disputed authorship or suspected plagiarism. The theoretical background to authorship attribution is presented in a step by step manner, and comprehensive reviews of the field are given in two specialist areas, the writings of William Shakespeare and his contemporaries, and the various writing styles seen in religious texts. The final chapter looks at the progress com |
|
|
|
|
|
|
|
| |