Robbert Prins

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Lexical ambiguity is an important source of inefficiency for wide-coverage HPSG parsing. In this paper, we propose a lexical analysis filter which removes unlikely lexical categories. The filter is implemented as a straightforward HMM n-gram POS-tagger, which computes the 'a posteriori' probability of each lexical category. A lexical category is removed if(More)
The Hidden Markov Model (HMM) for part-of-speech (POS) tagging is typically based on tag trigrams. As such it models local context but not global context, leaving long-distance syntactic relations unrepresented. Using n-gram models for n > 3 in order to incorporate global context is problematic as the tag sequences corresponding to higher order models will(More)
  • María Begoña, Villada Moirón, Rijksuniversiteit Groningen, J Hoeksema, J Odijk, I Sag +5 others
The work in this thesis has been carried out under the auspices of the Beha-vioral and Cognitive Neurosciences (BCN) research school, Groningen, and has been part of the pionier project Algorithms for Linguistic Processing supported by grant number 220-70-001 from the Netherlands Organization for Scientific Research (nwo). Preface During the years that I(More)
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