PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification and Annotation in Social Corpora using Multiple Relations
Yann Jacob, Ludovic Denoyer and Patrick Gallinari
In: CIKM 2011(2011).

Abstract

We consider the problem of learning to annotate documents with concepts or keywords in content information networks, where the documents may share multiple relations. The concepts associated to a document will depend both on its content and on its neighbors in the network through the different relations. We formalize this problem as single and multi-label classification in a multi-graph, the nodes being the documents and the edges representing the different relations. The proposed algorithm learns to weight the different relations according to their importance for the annotation task. We perform experiments on different corpora corresponding to different annotation tasks on scientific articles, emails and Flickr images and show how the model may take advantage of the rich relational information.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:9286
Deposited By:Ludovic Denoyer
Deposited On:21 February 2012