Ngram and Bayesian Classification of Documents for Topic and Authorship.
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| Title: | Ngram and Bayesian Classification of Documents for Topic and Authorship. |
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| Authors: | Clement, Ross1 clement@wmin.ac.uk, Sharp, David1 sharpd@wmin.ac.uk |
| Source: | Literary & Linguistic Computing. Nov2003, Vol. 18 Issue 4, p423-447. 25p. |
| Subjects: | Authorship, Archives, Literature, Speech, Function words (Grammar) |
| Abstract: | Large, real world, data sets have been investigated in the context of Authorship Attribution of real world documents. Ngram measures can be used to accurately assign authorship for long documents such as novels. A number of 5 (authors × 5 (movies) arrays of movie reviews were acquired from the Internet Movie Database. Both ngram and naive Bayes classifiers were used to classify along both the authorship and topic (movie) axes. Both approaches yielded similar results, and authorship was as accurately detected, or more accurately detected, than topic. Part of speech tagging and function-word lists were used to investigate the influence of structure on classification tasks on documents with meaning removed but grammatical structure intact. [ABSTRACT FROM AUTHOR] |
| Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 16435451 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Ngram and Bayesian Classification of Documents for Topic and Authorship. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Clement%2C+Ross%22">Clement, Ross</searchLink><relatesTo>1</relatesTo><i> clement@wmin.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Sharp%2C+David%22">Sharp, David</searchLink><relatesTo>1</relatesTo><i> sharpd@wmin.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Literary+%26+Linguistic+Computing%22">Literary & Linguistic Computing</searchLink>. Nov2003, Vol. 18 Issue 4, p423-447. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Authorship%22">Authorship</searchLink><br /><searchLink fieldCode="DE" term="%22Archives%22">Archives</searchLink><br /><searchLink fieldCode="DE" term="%22Literature%22">Literature</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Function+words+%28Grammar%29%22">Function words (Grammar)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Large, real world, data sets have been investigated in the context of Authorship Attribution of real world documents. Ngram measures can be used to accurately assign authorship for long documents such as novels. A number of 5 (authors &times 5 (movies) arrays of movie reviews were acquired from the Internet Movie Database. Both ngram and naive Bayes classifiers were used to classify along both the authorship and topic (movie) axes. Both approaches yielded similar results, and authorship was as accurately detected, or more accurately detected, than topic. Part of speech tagging and function-word lists were used to investigate the influence of structure on classification tasks on documents with meaning removed but grammatical structure intact. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Literary & Linguistic Computing is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=16435451 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/llc/18.4.423 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 423 Subjects: – SubjectFull: Authorship Type: general – SubjectFull: Archives Type: general – SubjectFull: Literature Type: general – SubjectFull: Speech Type: general – SubjectFull: Function words (Grammar) Type: general Titles: – TitleFull: Ngram and Bayesian Classification of Documents for Topic and Authorship. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Clement, Ross – PersonEntity: Name: NameFull: Sharp, David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2003 Type: published Y: 2003 Identifiers: – Type: issn-print Value: 02681145 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Literary & Linguistic Computing Type: main |
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