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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F13%3A10194631%21RIV14-GA0-11320___
rdf:type
n14:Vysledek skos:Concept
dcterms:description
Bilingual corpora offer a promising bridge between resource-rich and resource-poor languages, enabling the development of natural language processing systems for the latter. English is often selected as the resource-rich language, but another choice might give better performance. In this paper, we consider the task of unsupervised cross-lingual POS tagging, and construct a model that predicts the best source language for a given target language. In experiments on 9 languages, this model improves on using a single fixed source language. We then show that further improvements can be made by combining information from multiple source languages. Bilingual corpora offer a promising bridge between resource-rich and resource-poor languages, enabling the development of natural language processing systems for the latter. English is often selected as the resource-rich language, but another choice might give better performance. In this paper, we consider the task of unsupervised cross-lingual POS tagging, and construct a model that predicts the best source language for a given target language. In experiments on 9 languages, this model improves on using a single fixed source language. We then show that further improvements can be made by combining information from multiple source languages.
dcterms:title
Increasing the quality and quantity of source language data for unsupervised cross-lingual POS tagging. Increasing the quality and quantity of source language data for unsupervised cross-lingual POS tagging.
skos:prefLabel
Increasing the quality and quantity of source language data for unsupervised cross-lingual POS tagging. Increasing the quality and quantity of source language data for unsupervised cross-lingual POS tagging.
skos:notation
RIV/00216208:11320/13:10194631!RIV14-GA0-11320___
n14:predkladatel
n15:orjk%3A11320
n3:aktivita
n18:P
n3:aktivity
P(GBP103/12/G084)
n3:dodaniDat
n7:2014
n3:domaciTvurceVysledku
Duong, Long Thanh n16:2787865
n3:druhVysledku
n20:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n10:predkladatel
n3:idSjednocenehoVysledku
79505
n3:idVysledku
RIV/00216208:11320/13:10194631
n3:jazykVysledku
n22:eng
n3:klicovaSlova
tagging; lingual; cross; unsupervised; data; language; source; quantity; quality; increasing
n3:klicoveSlovo
n5:increasing n5:language n5:quality n5:quantity n5:lingual n5:unsupervised n5:data n5:tagging n5:source n5:cross
n3:kontrolniKodProRIV
[517EDADB7649]
n3:mistoKonaniAkce
Nagoya, Japan
n3:mistoVydani
Nagoya, Japan
n3:nazevZdroje
Proceedings of the 6th International Joint Conference on Natural Language Processing
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
4
n3:projekt
n9:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n7:2013
n3:tvurceVysledku
Pecina, Pavel Cook, Paul Bird, Steven Duong, Long Thanh
n3:typAkce
n21:CST
n3:zahajeniAkce
2013-10-14+02:00
s:numberOfPages
7
n11:hasPublisher
Asian Federation of Natural Language Processing
n8:isbn
978-4-9907348-0-0
n17:organizacniJednotka
11320