Improving Cross-domain Authorship Attribution by Combining Lexical and Syntactic Features: Notebook for PAN at CLEF 2019
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427040" target="_blank" >RIV/00216208:11320/19:10427040 - isvavai.cz</a>
Result on the web
<a href="https://research.rug.nl/en/publications/improving-cross-domain-authorship-attribution-by-combining-lexica" target="_blank" >https://research.rug.nl/en/publications/improving-cross-domain-authorship-attribution-by-combining-lexica</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Improving Cross-domain Authorship Attribution by Combining Lexical and Syntactic Features: Notebook for PAN at CLEF 2019
Original language description
Authorship attribution is a problem in information retrieval and computationallinguistics that involves attributing authorship of an unknown documentto an author within a set of candidate authors. Because of this, PAN-CLEF2019 organized a shared task that involves creating a computational model thatcan determine the author of a fanfiction story. The task is cross-domain becauseof the open set of fandoms to which the documents belong. Additionally, theset of candidate authors is also open since the actual author of a document maynot be among the candidate authors.We extracted character-level, word-level andsyntactic information from the documents in order to train a support vector machine.Our approach yields an overall macro-averaged F1 score of 0.687 on thedevelopment data of the shared task. This is an improvement of 18.7% over thecharacter-level lexical baseline. On the test data, our model achieves an overallmacro F1 score of 0.644.We compare different feature types and find that charactern-grams are the most informative feature type though all tested feature typescontribute to the performance of the model.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů