We show how one can add negation and disjunction to Prolog, with the property that there is no overhead in run time if we do not use the negation, and we only pay for the negation when we actually use it. The extensio...
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A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence ...
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A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence analysis. Viterbi algorithm has been proposed to compute the most probable value of these hidden states in regards to an observed data sequence. Constrained HMM extends this framework by adding some constraints on a HMM process run. In this paper, we propose to introduce constrained HMMs into Constraint programming. We propose new version of the Viterbi algorithm for this new framework. Several constraint techniques are used to reduce the search of the most probable value of hidden states of a constrained HMM. An implementation based on PRISM, a logicprogramming language for statistical modeling, is presented.
Online resources, large data repositories and streaming social network messages embed plenitudes of interesting knowledge, often of associative nature. A specific communicative context, such as the political debate in...
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We introduce BANpipe - a logic-based scripting language designed to model complex compositions of time consuming analyses. Its declarative semantics is described together with alternative operational semantics facilit...
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This paper describes an algorithm for the interpretation of temporal relations between events mentioned in narrative (such as which event occurs before another). These relations are decided through three different lev...
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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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.
Tabling of structured data is important to support dynamic programming in logic programs. Several existing tabling systems for Prolog do not efficiently deal with structured data, but duplicate part of the structured ...
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