Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistic...
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ISBN:
(纸本)9783939897316
Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results. Our methodology can be implemented using the probabilistic-logic PRISM system, developed by Sato et al, in a way that allows for practical applications.
This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and pr...
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This paper introduces Stochastic Definite Clause Grammars, a stochastic variant of the wellknown Definite Clause Grammars. The grammar formalism supports parameter learning from annotated or unannotated corpora and provides a mechanism for parse selection by means of statistical inference. Unlike probabilistic contextfree grammars, it is a context-sensitive grammar formalism and it has the ability to model cross-serial dependencies in natural language. SDCG also provides some syntax extensions which makes it possible to write more compact grammars and makes it straight-forward to add lexicalization schemes to a grammar.
This project aims to investigate biologically inspired, logic-statistic models with constraints. The complexity and expressiveness of models with different kinds of constraints will be examined and algorithms to effic...
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A class of Probabilistic Abductive Logic Programs (PALPs) is introduced and an implementation is developed in CHR for solving abductive problems, providing minimal explanations with their probabilities. Both all-expla...
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This paper presents an Ontology-based health care information extraction system - VnHIES. In the system, we develop and use two effective algorithms called "Semantic Elements Extracting Algorithm" and "...
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