A Multitask Learning Approach to Document Representation using Unlabeled Data
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In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentifica ...
With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...
With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...
Current document archives are enormously large and constantly increasing and that makes it practically impossible to make use of them efficiently. To analyze and interpret large volumes of speech and text of these archives in multiple languages and produce ...
With the rapid expansion in the use of computers for producing digitalized textual documents, the need of automatic systems for organizing and retrieving the information contained in large databases has become essential. In general, information retrieval s ...
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Clustering similar documents is a difficult task for text data mining. Difficulties stem especially from the way documents are translated into numerical vectors. In this chapter, we will present a method that uses Self Organizing Map (SOM) to cluster medic ...
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 ...
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 ...
