About: Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser     Goto   Sponge   Distinct   Permalink

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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 (en)
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 (en)
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 (en)
skos:notation
  • RIV/00216208:11320/12:10130050!RIV13-MSM-11320___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • R
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 139939
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/12:10130050
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • parser; dependency; ensemble; into; trees; dependency; constituency; combination; hybrid (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [02FE4FE587AB]
http://linked.open...v/mistoKonaniAkce
  • Avignon, France
http://linked.open...i/riv/mistoVydani
  • Avignon, France
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Žabokrtský, Zdeněk
  • Green, Nathan David
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Association for Computational Linguistics
https://schema.org/isbn
  • 978-1-937284-19-0
http://localhost/t...ganizacniJednotka
  • 11320
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