Structured Topic Models: Jointly Modeling Text with Its Accompanying Modalities - Couverture souple

Wang, Xuerui

 
9783639205572: Structured Topic Models: Jointly Modeling Text with Its Accompanying Modalities
Couverture souple
ISBN 10 : 363920557X ISBN 13 : 9783639205572
Editeur : VDM Verlag, 2009

Synopsis

The abundance of textual data in the information age poses an immense challenge for us: how to perform large-scale inference to understand and utilize this overwhelming amount of information. We develop effective and efficient statistical topic models for massive text collections by taking care of extra information from other modalities in addition to the text itself. Text documents are not just text, for example, research papers have author information, email messages contain social sender-recipient links, legislative resolutions are recorded with votes, and so on. These kinds of additional information are naturally interleaved with text. Most of the previous work, however, pay attention to only one modality at a time, and ignore the others. We present a series of probabilistic topic models to show how we can bridge multiple modalities of information, in a united fashion. Interestingly, joint inference over multiple modalities leads to many findings that can not be discovered from just one modality alone, which are clear evidence that we can better understand and utilize massive text collections when additional modalities are modeled jointly with text.

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Présentation de l'éditeur

The abundance of textual data in the information age poses an immense challenge for us: how to perform large-scale inference to understand and utilize this overwhelming amount of information. We develop effective and efficient statistical topic models for massive text collections by taking care of extra information from other modalities in addition to the text itself. Text documents are not just text, for example, research papers have author information, email messages contain social sender-recipient links, legislative resolutions are recorded with votes, and so on. These kinds of additional information are naturally interleaved with text. Most of the previous work, however, pay attention to only one modality at a time, and ignore the others. We present a series of probabilistic topic models to show how we can bridge multiple modalities of information, in a united fashion. Interestingly, joint inference over multiple modalities leads to many findings that can not be discovered from just one modality alone, which are clear evidence that we can better understand and utilize massive text collections when additional modalities are modeled jointly with text.

Biographie de l'auteur

Dr. Xuerui Wang is a Scientist at Yahoo! Labs, working on machine learning, topic models, social network analysis and online advertising. Dr. Andrew McCallum is an Associate Professor at University of Massachusetts, working on machine learning, natural language processing and information extraction.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
363920557X
ISBN 13 
9783639205572
Reliure
Broché
Nombre de pages
128
Coordonnées du fabricant
non disponible
Personne responsable
non disponible