Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learni...

and Objective Nearly at every patient visit medical documents are produced and stored in a medical record, often in unstructured form as free text. Growing amount of stored documents increases the need for effective and timely retrieval of information. We developed a multi-label classification system to categorize German language free text medical documents (e.g. discharge letters, clinical fin...

In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper shows that the accuracy of text classifiers trained with a small number of labeled documents can be improved by augmenting this small training set with a large pool of unlabeled documents. We present a theoretical argume...

We consider scalability issues of the text classification problem where by using (multi)-labeled training documents, we try to build classifiers that assign documents into classes permitting classification in multiple classes. A new class of classification problems; called ‘scalable’, is introduced, with applications on web mining. Scalable classification utilizes newly classified instances in ...

In this paper, we systematically study the problem of dataless hierarchical text classification. Unlike standard text classification schemes that rely on supervised training, dataless classification depends on understanding the labels of the sought after categories and requires no labeled data. Given a collection of text documents and a set of labels, we show that understanding the labels can b...

1 Introduction Most data collections from real world are in text format. Those data are considered semi structured data because they have a small organized structure. Modeling and implementing on semi structured data from recent data bases grows continually in the last years. More over, information retrieval applications, as indexing methods of text documents, have been adapted in order to work...

Text categorization is the problem of classifying text documents into a set of predefined classes. In this paper, we investigated three approaches to build a meta-classifier in order to increase the classification accuracy. The basic idea is to learn a metaclassifier to optimally select the best component classifier for each data point. The experimental results show that combining classifiers c...