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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F12%3A10130050%21RIV13-MSM-11320___
rdf:type
skos:Concept n10:Vysledek
rdfs:seeAlso
http://www.aclweb.org/anthology/W/W12/W12-0503
dcterms:description
Dependency parsing has made many advancements in recent years, in particular for English. There are a few dependency parsers that achieve comparable accuracy scores with each other but with very different types of errors. This paper examines creating a new dependency structure through ensemble learning using a hybrid of the outputs of various parsers. We combine all tree outputs into a weighted edge graph, using 4 weighting mechanisms. The weighted edge graph is the input into our ensemble system and is a hybrid of very different parsing techniques (constituent parsers, transition-based dependency parsers, and a graph-based parser). From this graph we take a maximum spanning tree. We examine the new dependency structure in terms of accuracy and errors on individual part-of-speech values. The results indicate that using a greater number of more varied parsers will improve accuracy results. The combined ensemble system, using 5 parsers based on 3 different parsing techniques, achieves an accuracy score Dependency parsing has made many advancements in recent years, in particular for English. There are a few dependency parsers that achieve comparable accuracy scores with each other but with very different types of errors. This paper examines creating a new dependency structure through ensemble learning using a hybrid of the outputs of various parsers. We combine all tree outputs into a weighted edge graph, using 4 weighting mechanisms. The weighted edge graph is the input into our ensemble system and is a hybrid of very different parsing techniques (constituent parsers, transition-based dependency parsers, and a graph-based parser). From this graph we take a maximum spanning tree. We examine the new dependency structure in terms of accuracy and errors on individual part-of-speech values. The results indicate that using a greater number of more varied parsers will improve accuracy results. The combined ensemble system, using 5 parsers based on 3 different parsing techniques, achieves an accuracy score
dcterms:title
Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser
skos:prefLabel
Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser
skos:notation
RIV/00216208:11320/12:10130050!RIV13-MSM-11320___
n10:predkladatel
n11:orjk%3A11320
n3:aktivita
n21:R
n3:aktivity
R
n3:dodaniDat
n8:2013
n3:domaciTvurceVysledku
n7:4217888 Green, Nathan David
n3:druhVysledku
n20:D
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n22:predkladatel
n3:idSjednocenehoVysledku
139939
n3:idVysledku
RIV/00216208:11320/12:10130050
n3:jazykVysledku
n16:eng
n3:klicovaSlova
parser; dependency; ensemble; into; trees; dependency; constituency; combination; hybrid
n3:klicoveSlovo
n9:into n9:hybrid n9:parser n9:ensemble n9:trees n9:dependency n9:constituency n9:combination
n3:kontrolniKodProRIV
[02FE4FE587AB]
n3:mistoKonaniAkce
Avignon, France
n3:mistoVydani
Avignon, France
n3:nazevZdroje
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n8:2012
n3:tvurceVysledku
Žabokrtský, Zdeněk Green, Nathan David
n3:typAkce
n18:CST
n3:zahajeniAkce
2012-04-23+02:00
s:numberOfPages
8
n15:hasPublisher
Association for Computational Linguistics
n14:isbn
978-1-937284-19-0
n17:organizacniJednotka
11320