A new anomalous text detection approach using unsupervised methods

Unsupervised detection of anomalous text

This thesis describes work on the detection of anomalous material in text without the use of training data. We use the term anomalous to refer to text that is irregular, or deviates significantly from its surrounding context. In this thesis we show that identifying such abnormalities in text can be viewed as a type of outlier detection because these anomalies will differ significantly from the ...

Unsupervised Learning-based Anomalous Arabic Text Detection

The growing dependence of modern society on the Web as a vital source of information and communication has become inevitable. However, the Web has become an ideal channel for various terrorist organisations to publish their misleading information and send unintelligible messages to communicate with their clients as well. The increase in the number of published anomalous misleading information o...

A new approach for video text detection

Text detection is fundamental to video information retrieval and indexing. Existing methods cannot handle well those texts with different contrast or embedded in a complex background. To handle these difficulties, this paper proposes an efficient text detection approach, which is based on invariant features, such as edge strength, edge density, and horizontal distribution. First, it applies edg...

A New Model for Text Coherence Evaluation using Statistical Characteristics

Optimization of Text Classification Using Supervised and Unsupervised Learning Approach

Text Classification, also known as text categorization, is the task of automatically allocating unlabeled documents into predefined categories. Text Classification means allocating a document to one or more categories or classes. The ability to accurately perform a classification task depends on the representations of documents to be classified. Text representations transform the textural docum...