Authorship and Time Attribution of Arabic Texts Using JGAAP

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11210%2F18%3A10365864" target="_blank" >RIV/00216208:11210/18:10365864 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-67056-0_16" target="_blank" >http://dx.doi.org/10.1007/978-3-319-67056-0_16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-67056-0_16" target="_blank" >10.1007/978-3-319-67056-0_16</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Authorship and Time Attribution of Arabic Texts Using JGAAP

Popis výsledku v původním jazyce

One basic task in Natural Language processing is text classification, such as sorting documents by their content. A less well-known variant on this task is classifying documents by inferred metadata, such as the document&apos;s (inferred) language, date of composition or authorship. Authorship attribution is a well-studied problem, but most of the work done has been in major European languages such as English. [Notable exceptions who have studied Arabic, in particular, include. We present a study selected from a new corpus (CLAUDia) containing nearly a half-billion words of Arabic text using a standard authorship analysis tool (JGAAP) to study the effects of author, genre, and time of composition on writing style and by extension on classification. We have selected a subcorpus balanced to permit comparisons between genres as well as between time periods to see how best-performing methods change with genre and time. We also provide an analysis of a larger variety of different feature sets than has previously been done for Arabic.

Název v anglickém jazyce

Authorship and Time Attribution of Arabic Texts Using JGAAP

Popis výsledku anglicky

One basic task in Natural Language processing is text classification, such as sorting documents by their content. A less well-known variant on this task is classifying documents by inferred metadata, such as the document&apos;s (inferred) language, date of composition or authorship. Authorship attribution is a well-studied problem, but most of the work done has been in major European languages such as English. [Notable exceptions who have studied Arabic, in particular, include. We present a study selected from a new corpus (CLAUDia) containing nearly a half-billion words of Arabic text using a standard authorship analysis tool (JGAAP) to study the effects of author, genre, and time of composition on writing style and by extension on classification. We have selected a subcorpus balanced to permit comparisons between genres as well as between time periods to see how best-performing methods change with genre and time. We also provide an analysis of a larger variety of different feature sets than has previously been done for Arabic.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA13-28220S" target="_blank" >GA13-28220S: Struktury kultury: Arabsko-islámská kultura prismatem korpusové lingvistiky</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Intelligent Natural Language Processing: Trends and Applications

ISBN

978-3-319-67056-0

Počet stran výsledku

25

Strana od-do

325-349

Počet stran knihy

776

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

—