19 research outputs found
Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context
An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data
Fifth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools Aarhus, Denmark, October 8-11, 2004
This booklet contains the proceedings of the Fifth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, October 8-11, 2004. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop0
Bayesian Modelling Approaches for Quantum States -- The Ultimate Gaussian Process States Handbook
Capturing the correlation emerging between constituents of many-body systems
accurately is one of the key challenges for the appropriate description of
various systems whose properties are underpinned by quantum mechanical
fundamentals. This thesis discusses novel tools and techniques for the
(classical) modelling of quantum many-body wavefunctions with the ultimate goal
to introduce a universal framework for finding accurate representations from
which system properties can be extracted efficiently. It is outlined how
synergies with standard machine learning approaches can be exploited to enable
an automated inference of the most relevant intrinsic characteristics through
rigorous Bayesian regression techniques. Based on the probabilistic framework
forming the foundation of the introduced ansatz, coined the Gaussian Process
State, different compression techniques are explored to extract numerically
feasible representations of relevant target states within stochastic schemes.
By following intuitively motivated design principles, the resulting model
carries a high degree of interpretability and offers an easily applicable tool
for the numerical study of quantum systems, including ones which are
notoriously difficult to simulate due to a strong intrinsic correlation. The
practical applicability of the Gaussian Process States framework is
demonstrated within several benchmark applications, in particular, ground state
approximations for prototypical quantum lattice models, Fermi-Hubbard models
and models, as well as simple ab-initio quantum chemical systems.Comment: PhD Thesis, King's College London, 202 page
Geo-Tagged Video Management: Storage, Queries and Streaming
Ph.DDOCTOR OF PHILOSOPH