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Tagging named entities in Croatian tweets [Elektronski vir]Baksa, Krešimir ...Obstoječa orodja za prepoznavanje imenskih entitet, ki so tipično izdelana za formalna besedila, napisana v standardnem jeziku (npr. novice, eseji ali pravna besedila), ne delujejo dobro na vsebinah, ... ki jih ustvarjajo uporabniki (npr. tviti). V prispevku predstavimo voden način za prepoznavanje in klasifikacijo imenskih entitet v hrvaških tvitih. Primerjava treh različnih modelov za označevanje zaporedij (HMM, CRF in SVM) je pokazala, da je najboljši model za to nalogo CRF, ki doseže za mikropovprečno mero F1 rezultat prek 87 %. Pokažemo tudi, da najboljši model za prepoznavanje hrvaških imenskih entitet v standardnem jeziku deluje mnogo slabše kot naši modeli za prepoznavanje imenskih entitet v tvitih.Source: Slovenščina 2.0 [Elektronski vir] : empirične, aplikativne in interdisciplinarne raziskave. - ISSN 2335-2736 (Letn. 4, št. 1, 2016, str. 20-41)Type of material - e-articlePublish date - 2016Language - englishCOBISS.SI-ID - 63582050
Author
Baksa, Krešimir |
Dolović, Dino |
Glavaš, Goran |
Šnajder, Jan
Topics
hrvaščina |
družabni mediji |
mediji |
tviti |
prepoznavanje imenskih entitet |
strojno učenje |
luščenje podatkov |
Croatian language |
social media |
tweets |
named entity recognition |
machine learning |
information extraction
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Baksa, Krešimir | |
Dolović, Dino | |
Glavaš, Goran | |
Šnajder, Jan |
Source: Personal bibliographies
and: SICRIS
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