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Phrasal Parsing by Using Data-Driven PoS Taggers
2001 (English)In: Proceedings of the Conference on Recent Advances in Natural Language Processing: Euro Conference RANLP-2001, 2001, 166-173 p.Conference paper (Refereed)
Abstract [en]

Three data-driven algorithms are applied to shallow parsing of Swedish texts by using PoS taggers as the basis for parsing. The constituent structure is represented by nine types of phrases in a hierarchical structure containing labels for every constituent type the token belongs to. The results show that best performance can be obtained by training on the basis of PoS tags with labels marking the phrasal constituents without considering the words themselves. Transformation-based learning gives highest accuracy (94.44%) followed by the Maximum Entropy framework (mxpost) (92.47%) and the Hidden Markov model (TnT) (92.42%).

Place, publisher, year, edition, pages
2001. 166-173 p.
Keyword [en]
chunking, machine learning, PoS tagger
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
URN: urn:nbn:se:uu:diva-19646OAI: oai:DiVA.org:uu-19646DiVA: diva2:47418
Recent Advances in Natural Language Processing
Available from: 2006-11-30 Created: 2006-11-30 Last updated: 2016-03-08

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Megyesi, Beata
Language Technology (Computational Linguistics)

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ReferencesLink to record
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