Improving Cross-domain Authorship Attribution by Combining Lexical and Syntactic Features: Notebook for PAN at CLEF 2019

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Improving Cross-domain Authorship Attribution by Combining Lexical and Syntactic Features: Notebook for PAN at CLEF 2019

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Improving Cross-domain Authorship Attribution by Combining Lexical and Syntactic Features: Notebook for PAN at CLEF 2019

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

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Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů