Search Results

This paper proposes a system enforcing content-based message filtering for On-line Social Networks (OSNs). The system allows OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, ...

The geographic database (GDB) is the backbone of the geographic information system (GIS). Indeed, all kinds of data managements are based and strongly affected by the type, relevancy and scope of the stored data. Nevertheless, this dataset is sometimes ...

Text representation is a preliminary step to text filtering, while VSM is the most commonly used method in this field. However, the document feature set, which produced by VSM, usually has a very high dimensionality. As a result, the distribution of ...

We present a simple approach for the classification of "noisy" documents using bigrams and named entities. The approach combines conventional feature selection with a contextual approach to filter out passages around selected features. Originally ...

Information is often represented in text form and classified into categories. Unfortunately, automatic classifiers often conduct misclassifications. One of the reasons is that the documents for training the classifiers are mainly from the categories, ...

The semantic analysis of the Chinese news video with the help of World Wide Web is proposed. First, we segment the news video into a series of story units. Second, we extract the key phrases from the corresponding ASR transcript of news story, and ...

Feature selection is a very important step in text preprocessing, a good selected feature subset can get the same performance than using full features, at the same time, it reduced the learning time. To make our system fit for the application and to ...

Information Retrieval (IR) has played a very important role in our modern life. However, the results of search engines are not satisfactory for human intelligent activities. The platform proposed in this paper tried to solve the problems from three ...

Most existing studies of text classification assume that the training data are completely labeled. In reality, however, many information retrieval problems can be more accurately described as learning a binary classifier from a set of incompletely ...

We describe an efficient, robust method for selecting and optimizing terms for a classification or filtering task. Terms are extracted from positive examples in training data based on several alternative term-selection algorithms, then combined ...

This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of ...

The techniques of information retrieval and information extraction are complementary, but to date there has been little concrete work aimed at integrating the two. We describe how each of these techniques contributes to the process of transferring ...