PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Weather Talk - extracting weather information by text mining
Vasileios Lampos
(2008) Masters thesis, University of Bristol.

Abstract

The main aim of this project was to design and implement a system able to infer the weather state of a location for a specific date by applying Bayesian inference models and statistical analysis on web observations. Additionally, we investigated various linear combinations of probabilistic schemes where traffic information, previous day's weather or a weather prior probability contribute to the final decision. As a final extension, we visualised the weather inference results on a map. Software packages and a weather ontology were developed for data collection and preprocessing. Parameterised Bayesian belief networks formed the expression of probabilistic correlation between the inferred and the official weather observations. During training, we decide the optimal parameters and then test their absolute and relative performance. Experimental results indicate that the absolute and relative (p-values) performance in most of the schemes is significant. As a result, one may assume that similar or even more sophisticated information extraction models on different contexts will be able to deliver useful conclusions.

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EPrint Type:Thesis (Masters)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:6928
Deposited By:Vasileios Lampos
Deposited On:21 April 2010