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Author Kiefer, Bernd ♦ Krieger, Hans-Ulrich ♦ Prescher, Detlef
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Probability Model ♦ Novel Approach ♦ Novel Disambiguation Method ♦ Unification-based Grammar ♦ Ubg Side ♦ Context-free Model ♦ Exact-match Task ♦ Mid-size Ubg ♦ Cubic Time ♦ Unsupervised Training ♦ Context-free Reading ♦ Probability Distribution
Description We present a novel disambiguation method for unification-based grammars (UBGs). In contrast to other methods, our approach obviates the need for probability models on the UBG side in that it shifts the responsibility to simpler context-free models, indirectly obtained from the UBG. Our approach has three advantages: (i) training can be effectively done in practice, (ii) parsing and disambiguation of context-free readings requires only cubic time, and (iii) involved probability distributions are mathematically clean. In an experiment for a mid-size UBG, we show that our novel approach is feasible. Using unsupervised training, we achieve 88% accuracy on an exact-match task.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2002-01-01
Publisher Institution IN PROC. OF COLING 2002