This HTML5 document contains 46 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n8http://localhost/temp/predkladatel/
n7http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n15http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216208%3A11320%2F11%3A10107806%21RIV12-GA0-11320___/
n5http://linked.opendata.cz/resource/domain/vavai/projekt/
n12http://linked.opendata.cz/resource/domain/vavai/subjekt/
n9http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n20http://bibframe.org/vocab/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n14http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F11%3A10107806%21RIV12-GA0-11320___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
We propose a method of automatic identification of various error types in machine translation output. The approach is mostly based on monolingual word alignment of the hypothesis and the reference translation. In addition to common lexical errors misplaced words are also detected. A comparison to manually classified MT errors is presented. Our error classification is inspired by that of Vilar et al. (2006), although distinguishing some of their categories is beyond the reach of the current version of our system. We propose a method of automatic identification of various error types in machine translation output. The approach is mostly based on monolingual word alignment of the hypothesis and the reference translation. In addition to common lexical errors misplaced words are also detected. A comparison to manually classified MT errors is presented. Our error classification is inspired by that of Vilar et al. (2006), although distinguishing some of their categories is beyond the reach of the current version of our system.
dcterms:title
Automatic Translation Error Analysis Automatic Translation Error Analysis
skos:prefLabel
Automatic Translation Error Analysis Automatic Translation Error Analysis
skos:notation
RIV/00216208:11320/11:10107806!RIV12-GA0-11320___
n9:predkladatel
n12:orjk%3A11320
n3:aktivita
n16:P
n3:aktivity
P(GAP406/11/1499), P(GPP406/10/P259)
n3:cisloPeriodika
1
n3:dodaniDat
n14:2012
n3:domaciTvurceVysledku
n7:3526569 n7:2630176 n7:9363661
n3:druhVysledku
n17:J
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
187598
n3:idVysledku
RIV/00216208:11320/11:10107806
n3:jazykVysledku
n11:eng
n3:klicovaSlova
analysis; error; translation; automatic
n3:klicoveSlovo
n4:translation n4:automatic n4:analysis n4:error
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[7C373FD1B04D]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n13:AI
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n5:GPP406%2F10%2FP259 n5:GAP406%2F11%2F1499
n3:rokUplatneniVysledku
n14:2011
n3:svazekPeriodika
6836
n3:tvurceVysledku
Zeman, Daniel Fishel, Mark Bojar, Ondřej Berka, Jan
s:issn
0302-9743
s:numberOfPages
8
n20:doi
10.1007/978-3-642-23538-2
n8:organizacniJednotka
11320