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Digital Library of the
European Council for Modelling and Simulation |
Title: |
Multi-Resolution Modelling Of Topic Relationships In Semantic Space |
Authors: |
Wang Wei, Andrzej Bargiela |
Published in: |
(2009).ECMS
2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M.
Doncel Pedrera. European Council for Modeling and Simulation. doi:10.7148/2009 ISBN: 978-0-9553018-8-9 23rd
European Conference on Modelling and Simulation, Madrid, June
9-12, 2009 |
Citation
format: |
Wei, W., & Bargiela, A.
(2009). Multi-Resolution Modelling Of Topic Relationships In Semantic Space.
ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp. 813-819). European Council for
Modeling and Simulation. doi:10.7148/2009-0813-0819 |
DOI: |
http://dx.doi.org/10.7148/2009-0813-0819 |
Abstract: |
Recent
techniques for document modelling provide means for transforming document
representation in high dimensional word space to low dimensional semantic
space. The representation with coarse resolution is often regarded as being
able to capture intrinsic semantic struc- ture of the original documents.
Probabilistic topic mod- els for document modelling attempt to search for
richer representations of the structure of linguistic stimuli and as such
support the process of human cognition. The topics inferred by the
probabilistic topic models (latent topics) are represented as probability
distributions over words. Although they are interpretable, the interpreta-
tion is not sufficiently straightforward for human under- standing. Also,
perhaps more importantly, relationships between the topics are difficult, if
not impossible to in- terpret. Instead of directly operating on the latent
top- ics, we extract topics with labels from a document col- lection and
represent them using fictitious documents. Having trained the probabilistic
topic models, we pro- pose a method for deriving relationships (more general
or more specific) between the extracted topics in the se- mantic space. To
ensure a reasonable accuracy of mod- eling in a given semantic space we have
conducted ex- periments with various dimensionality of the semantic space to
identify optimal parameter settings in this con- text. Evaluation and
comparison show that our method outperforms the existing methods for learning
concept or topic relationships using same dataset. |
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