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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Veröffentlicht in:Natural language engineering Jg. 23; H. 5; S. 789 - 806
Hauptverfasser: BJÖRKLUND, JOHANNA, ZECHNER, NIKLAS
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cambridge, UK Cambridge University Press 01.09.2017
Schlagworte:
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
ISSN:1351-3249, 1469-8110, 1469-8110
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1351-3249
1469-8110
1469-8110
DOI:10.1017/S1351324917000249