About: Unsupervised Learning of Rules for Morphological Disambiguation     Goto   Sponge   NotDistinct   Permalink

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Description
  • Současné nástroje pro morfologickou desambiguaci využívají manuálně vytvořených pravidel anebo jsou trénovány na morfologicky označkovaných datech. Článek představuje novou metodu učení pravidel pro morfologickou desambiguaci, která se učí pouze na neoznačkovaných datech s využitím induktivního logického programování a aktivního učení. Získaná pravidla vykazují velmi slibnou přesnost. V článku jsou diskutována i pravděpodobná omezení navrhované metody. (cs)
  • State-of-the-art rule-based tools for morphological disambiguation use either manually crafted rules or rules learnt from manually annotated data. This paper presents a new method of learning rules for morphological disambiguation using only unannotated data. The inductive logic programming and active learning are employed. The induced rules display very promising acurracy. Also the probable limitations of the proposed method are discussed.
  • State-of-the-art rule-based tools for morphological disambiguation use either manually crafted rules or rules learnt from manually annotated data. This paper presents a new method of learning rules for morphological disambiguation using only unannotated data. The inductive logic programming and active learning are employed. The induced rules display very promising acurracy. Also the probable limitations of the proposed method are discussed. (en)
Title
  • Učení bez učitele morfologických pravidel pro desambiguaci (cs)
  • Unsupervised Learning of Rules for Morphological Disambiguation
  • Unsupervised Learning of Rules for Morphological Disambiguation (en)
skos:prefLabel
  • Učení bez učitele morfologických pravidel pro desambiguaci (cs)
  • Unsupervised Learning of Rules for Morphological Disambiguation
  • Unsupervised Learning of Rules for Morphological Disambiguation (en)
skos:notation
  • RIV/00216224:14330/04:00010303!RIV08-MSM-14330___
http://linked.open.../vavai/riv/strany
  • 211-216
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 143300003)
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
  • 591482
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/04:00010303
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • morphological disambiguation; tagging; morphological tagging; unsupervised learning (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9A077DA1841E]
http://linked.open...v/mistoKonaniAkce
  • Brno, Czech Republic
http://linked.open...i/riv/mistoVydani
  • Berlin
http://linked.open...i/riv/nazevZdroje
  • Text, Speech and Dialogue: 7th International Conference, TSD2004
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Šmerk, Pavel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 3-540-23049-1
http://localhost/t...ganizacniJednotka
  • 14330
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