Translating time expression into absolute time points or durations is a challenge for natural languages processing such as text mining and text understanding in general. We present a constraint logic language CLP(Time...
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A new language is introduced for describing hypotheses about fluctuations of measurable properties in streams of timestamped data, and as prime example, we consider trends of emotions in the constantly flowing stream ...
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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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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 logic programming language for statistical modeling, is presented.
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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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 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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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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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 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.
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