Stefan Riezler
Probabilistic Constraint Logic Programming
Arbeitspapiere des SFB 340, Bericht Nr. 117 (1997), 35pp.
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Abstract
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient
retrieval of most probable analyses. These questions have been answered
satisfactorily only for probabilistic regular and context-free
models. We address these problems for a more expressive
probabilistic constraint logic programming model.
We present a log-linear probability model for probabilistic constraint
logic programming. On top of this model we define an algorithm to
estimate the parameters and to select the properties of log-linear
models from incomplete data. This algorithm is an extension of the
improved iterative scaling algorithm of
Della-Pietra (1995). Our algorithm applies to log-linear models in
general and is accompanied with suitable approximation methods when
applied to large data spaces. Furthermore, we present an approach for searching
for most probable analyses of the probabilistic constraint logic
programming model. This method can be applied to the ambiguity resolution
problem in natural language processing applications.
Graduiertenkolleg ILS
Seminar für Sprachwissenschaft
Eberhard-Karls-Universität Tübingen
Wilhelmstraße 113
72074 Tübingen
Germany
riezler@sfs.nphil.uni-tuebingen.de