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Literary detective work on the computer / / Michael P. Oakes
| Literary detective work on the computer / / Michael P. Oakes |
| Autore | Oakes Michael P. |
| Pubbl/distr/stampa | Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 |
| Descrizione fisica | 1 online resource (293 p.) |
| Disciplina | 410.285 |
| Collana | Natural Language Processing |
| Soggetto topico |
Computational linguistics - Research
Imitation in literature Plagiarism |
| Soggetto genere / forma | Electronic books. |
| ISBN | 90-272-7013-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910458564203321 |
Oakes Michael P.
|
||
| Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Literary detective work on the computer / / Michael P. Oakes
| Literary detective work on the computer / / Michael P. Oakes |
| Autore | Oakes Michael P. |
| Pubbl/distr/stampa | Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 |
| Descrizione fisica | 1 online resource (293 p.) |
| Disciplina | 410.285 |
| Collana | Natural Language Processing |
| Soggetto topico |
Computational linguistics - Research
Imitation in literature Plagiarism |
| ISBN | 90-272-7013-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910790909603321 |
|
Oakes Michael P.
|
||
| Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Literary detective work on the computer / / Michael P. Oakes
| Literary detective work on the computer / / Michael P. Oakes |
| Autore | Oakes Michael P. |
| Pubbl/distr/stampa | Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 |
| Descrizione fisica | 1 online resource (293 p.) |
| Disciplina | 410.285 |
| Collana | Natural Language Processing |
| Soggetto topico |
Computational linguistics - Research
Imitation in literature Plagiarism |
| ISBN | 90-272-7013-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910826746603321 |
|
Oakes Michael P.
|
||
| Amsterdam (Netherlands) ; ; Philadelphia, Pennsylvania : , : John Benjamins Publishing Company, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||