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Description
  • We present an unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus. In contrast to the existing state-of-the-art approach of Das and Petrov, we have developed a substantially simpler method by automatically identifying %22good%22 training sentences from the parallel corpus and applying self-training. In experimental results on eight languages, our method achieves state-of-the-art results.
  • We present an unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus. In contrast to the existing state-of-the-art approach of Das and Petrov, we have developed a substantially simpler method by automatically identifying %22good%22 training sentences from the parallel corpus and applying self-training. In experimental results on eight languages, our method achieves state-of-the-art results. (en)
Title
  • Simpler unsupervised POS tagging with bilingual projections
  • Simpler unsupervised POS tagging with bilingual projections (en)
skos:prefLabel
  • Simpler unsupervised POS tagging with bilingual projections
  • Simpler unsupervised POS tagging with bilingual projections (en)
skos:notation
  • RIV/00216208:11320/13:10194630!RIV14-GA0-11320___
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  • P(GBP103/12/G084)
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  • 105131
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  • RIV/00216208:11320/13:10194630
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  • projections; bilingual; with; tagging; unsupervised; simpler (en)
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http://linked.open...ontrolniKodProRIV
  • [0E80AF4A5B6B]
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  • Sofija, Bulgaria
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  • Sofija, Bulgaria
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  • Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics
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  • Pecina, Pavel
  • Bird, Steven
  • Cook, Paul
  • Duong, Long Thanh
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • Association for Computational Linguistics
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  • 978-1-937284-50-3
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  • 11320
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