Decision makers in enterprises cannot handle information flooding without serious problems. A market data information system (MAIS), which is the foundation of a decision support system for German energy trading, uses search and filter components to provide decision-relevant information from Web-documents for enterprises. The already implemented filter component in form of a Multilayer Perceptr...

Supervised text classification algorithms require a large number of documents labeled by humans, that involve a laborintensive and time consuming process. In this paper, we propose a weakly supervised algorithm in which supervision comes in the form of labeling of Latent Dirichlet Allocation (LDA) topics. We then use this weak supervision to “sprinkle” artificial words to the training documents...

This paper presents a weakly-supervised transfer learning based text categorization method, which does not need to tag new training documents when facing classification tasks in new area. Instead, we can take use of the already tagged documents in other domains to accomplish the automatic categorization task. By extracting linguistic information such as part-of-speech, semantic, co-occurrence o...

Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that...

Automatically identifying the research areas of academic/industry researchers is an important task for building expertise organizations or search systems. In general, this task can be viewed as text classification that generates a set of research areas given the expertise of a researcher like documents of publications. However, this task is challenging because the evidence of a research area ma...

Understanding user interests from text documents can provide support to personalized information recommendation services. Typically, these services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. Traditional keyword-based approaches are unable to capture the semantics of the user interests. This work p...

Humans are remarkably adept at classifying text documents into categories. For instance, while reading a news story, we are rapidly able to assess whether it belongs to the domain of finance, politics or sports. Automating this task would have applications for content-based search or filtering of digital documents. To this end, it is interesting to investigate the nature of information humans u...

Automatic text classification is a popular research topic in text mining. Automatic text classification is an eminent field of research in text mining, which is tries to automatically classify the text documents into pre-specified categories. Text mining involves several pre-processing and classification techniques. In this paper, we have analysed several feature selection methods with support ...

With the significant growth in the number of available electronic documents on the Internet, intranets, and digital libraries, the need for developing effective methods and systems to index and organize E-documents is felt more than ever. In this paper we introduce a new method for automatic text classification for categorizing E-documents by utilizing classification metadata of books, journals...

This paper presents the design of a system for feature extraction and classification of news articles from Croatian news sources. An overview of supervised and unsupervised text classification and clustering machine learning techniques is presented. The techniques described are those most widely used for text classification tasks. The paper discusses a number of issues particular to text classi...