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Noisy Text Categorization

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A Multitask Learning Approach to Document Representation using Unlabeled Data

Samy Bengio, Mikaela Keller

Text categorization is intrinsically a supervised learning task, which aims at relating a given text document to one or more predefined categories. Unfortunately, labeling such databases of documents is a painful task. We present in this paper a method tha ...

IDIAP2006

Noisy Text Categorization

Alessandro Vinciarelli

This work presents categorization experiments performed over noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media other than digital texts (e.g. transcriptions of speech recordings extracted with ...

2005

Extracting Information from Multimedia Meeting Collections

Daniel Gatica-Perez, Samy Bengio, Dong Zhang

Multimedia meeting collections, composed of unedited audio and video streams, handwritten notes, slides, and electronic documents that jointly constitute a raw record of complex human interaction processes in the workplace, have attracted interest due to t ...

IDIAP2005

Extracting Information from Multimedia Meeting Collections

Daniel Gatica-Perez, Samy Bengio, Dong Zhang

2005

A Thousand Words in a Scene

Daniel Gatica-Perez, Jean-Marc Odobez, Pedro Manuel Da Silva Quelhas

This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our work attempts to elucidate (1) whether a text-like \emph{bag-of-visterms} represent ...

IDIAP2005

Noisy Text Categorization

Alessandro Vinciarelli

This work presents a system for the categorization of noisy texts. By noisy it is meant any text obtained through an extraction process (affected by errors) from media different than digital texts. We show that, even with an average Word Error Rate of arou ...

2004

Noisy Text Categorization

Alessandro Vinciarelli

IDIAP2004

Thematic Annotation: extracting concepts out of documents

Martin Rajman

Semantic document annotation may be useful for many tasks. In particular, in the framework of the MDM project(http://www.issco.unige.ch/projects/im2/mdm/), topical annotation -- i.e. the annotation of document segments with tags identifying the topics disc ...

2004

Noisy Text Clustering

David Grangier, Alessandro Vinciarelli

This work presents document clustering experiments performed over noisy texts (i.e. text that have been extracted through an automatic process like speech or character recognition). The effect of recognition errors on different clustering techniques is mea ...

IDIAP2004

On Automatic Annotation of Images with Latent Space Models

Daniel Gatica-Perez, Florent Monay Michaud

Image auto-annotation, i.e., the association of words to whole images, has attracted considerable attention. In particular, unsupervised, probabilistic latent variable models of text and image features have shown encouraging results, but their performance ...

IDIAP2003