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Slovenščina 2.0: Empirične, aplikativne in interdisciplinarne raziskave
Article . 2013 . Peer-reviewed
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Article . 2012
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Article . 2012
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Razpoznavanje imenskih entitet v slovenskem besedilu

Authors: Štajner, Tadej; Erjavec, Tomaž; Krek, Simon;

Razpoznavanje imenskih entitet v slovenskem besedilu

Abstract

Članek predstavlja algoritem in implementacijo programa za razpoznavanje imen v slovenskem jeziku s pomočjo strojnega učenja. Nadzorovani pristop na osnovi pogojnih naključnih polj je naučen na označenem korpusu ssj500k. V korpusu, ki je prosto dostopen pod licenco Creative Commons CC-BY-NC-SA, so pri besednih pojavnicah poleg oblikoskladenjskih oznak in lem označena tudi imena organizacij, osebna, zemljepisna ter stvarna imena. Članek predstavlja vpliv na natančnost razpoznavanja ob uporabi oblikoskladenjskih oznak, leksikonov in konjunkcij sosednjih lastnosti. Ena od ugotovitev raziskave je, da so oblikoskladenjske oznake pri razpoznavanju entitet koristne. V kombinaciji z vsemi ostalimi lastnostmi doseže sistem na testni množici 74% natančnost in 72% priklic, pri čemer so najbolje razpoznana osebna imena, sledijo jim zemljepisna ter organizacijska in nazadnje stvarna imena. Novo spoznanje članka je tudi to, da lahko z delitvijo razreda vseh stvarnih imen na organizacije in preostala stvarna imena dosežemo boljše rezultate prepoznavanja tudi pri drugih razredih. Preizkusi na neodvisno označenih korpusi kažejo dobro posplošenost modela za osebna in zemljepisna imena. Programska oprema, narejena v raziskavi, je prosto dostopna pod licenco Apache 2.0 na naslovu http://ailab.ijs.si/~tadej/slner.zip, razvojne različice pa so na voljo na naslovuhttps://github.com/tadejs/slner.

Keywords

prepoznavanje lastnih imen, izločanje entitet, P1-1091, procesiranje naravnega jezika, Philology. Linguistics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average
Green
Published in a Diamond OA journal
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