Syntactic methods for topic-independent authorship attribution

The efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are ‘deep’ in the sense that they are derived by parsing the subject texts, in contrast to ‘shallow’ syntactic fe...

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Published in:Natural language engineering Vol. 23; no. 5; pp. 789 - 806
Main Authors: BJÖRKLUND, JOHANNA, ZECHNER, NIKLAS
Format: Journal Article
Language:English
Published: Cambridge, UK Cambridge University Press 01.09.2017
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ISSN:1351-3249, 1469-8110, 1469-8110
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Abstract The efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are ‘deep’ in the sense that they are derived by parsing the subject texts, in contrast to ‘shallow’ syntactic features for which a part-of-speech analysis is enough. The experiments are made on two corpora of online texts and one corpus of novels written around the year 1900. The classification tasks include classical closed-world authorship attribution, identification of separate texts among the works of one author, and cross-topic authorship attribution. In the first tasks, the feature sets were fairly evenly matched, but for the last task, the syntax-based feature set outperformed the baseline feature set. These results suggest that, compared to lexical features, syntactic features are more robust to changes in topic.
AbstractList The efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are ‘deep’ in the sense that they are derived by parsing the subject texts, in contrast to ‘shallow’ syntactic features for which a part-of-speech analysis is enough. The experiments are made on two corpora of online texts and one corpus of novels written around the year 1900. The classification tasks include classical closed-world authorship attribution, identification of separate texts among the works of one author, and cross-topic authorship attribution. In the first tasks, the feature sets were fairly evenly matched, but for the last task, the syntax-based feature set outperformed the baseline feature set. These results suggest that, compared to lexical features, syntactic features are more robust to changes in topic.
Author BJÖRKLUND, JOHANNA
ZECHNER, NIKLAS
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CitedBy_id crossref_primary_10_1093_llc_fqad061
crossref_primary_10_1177_0165551519863350
crossref_primary_10_1007_s00521_022_07944_5
crossref_primary_10_1371_journal_pone_0267590
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DocumentTitleAlternate Syntactic methods for authorship attribution
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SubjectTerms Artificial intelligence
Attribution
Authoring
Authorship
Classification
Computational linguistics
Computer generated language analysis
Discriminant analysis
Efficacy
Experiments
Linguistics
Novels
Parsing
Plagiarism
Principal components analysis
Punctuation
Speech
Syntactic analysis
Syntactic features
Syntax
Texts
Topics
Word frequency
Title Syntactic methods for topic-independent authorship attribution
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Volume 23
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