In this extended abstract, a novel approach is proposed for text pattern recognition. Instead of the traditional models which are mainly based on the frequency of keywords for text document classification, we introduce a new graph theory model which is constructed based on both information about frequency and position of keywords. We applied this new idea to the detection of fraudulent emails written by the same person, and plagiarized publications. The results on these case studies show that this new method performs much better than traditional methods. I
This thesis presents the application of various classification techniques on text documents. Since t...
Abstract Text Mining is a research area of retrieving high quality hidden information such as patter...
Abstract. We discuss a probabilistic graphical model that works for recognizing three types of text ...
Plagiarism is a form of academic misconduct. It has increased rapidly because it is now quick and ea...
The main topic of this doctoral dissertation is the extraction of valuable in- formation associate...
In this paper a new graph-based model is proposed for the representation of textual documents. Graph...
Text classification is the problem of assigning pre-defined class labels to incoming, unclassified d...
Abstract The rapid proliferation of the World Wide Web has increased the importance and prevalence o...
Text representation models are the fundamental basis for information retrieval and text mining tasks...
For several decades graphs act as a powerful and flexible representation formalism in pattern recogn...
It is well known that supervised text classification methods need to learn from many labeled exampl...
A common and standard approach to model text document is bag-of-words. This model is suitable for ca...
In text documents data mining techniques have been proposed for mining useful patterns. But how to e...
This thesis surveys use of graph theory and algorithms in information retrieval. It provides an intr...
Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to t...
This thesis presents the application of various classification techniques on text documents. Since t...
Abstract Text Mining is a research area of retrieving high quality hidden information such as patter...
Abstract. We discuss a probabilistic graphical model that works for recognizing three types of text ...
Plagiarism is a form of academic misconduct. It has increased rapidly because it is now quick and ea...
The main topic of this doctoral dissertation is the extraction of valuable in- formation associate...
In this paper a new graph-based model is proposed for the representation of textual documents. Graph...
Text classification is the problem of assigning pre-defined class labels to incoming, unclassified d...
Abstract The rapid proliferation of the World Wide Web has increased the importance and prevalence o...
Text representation models are the fundamental basis for information retrieval and text mining tasks...
For several decades graphs act as a powerful and flexible representation formalism in pattern recogn...
It is well known that supervised text classification methods need to learn from many labeled exampl...
A common and standard approach to model text document is bag-of-words. This model is suitable for ca...
In text documents data mining techniques have been proposed for mining useful patterns. But how to e...
This thesis surveys use of graph theory and algorithms in information retrieval. It provides an intr...
Text mining, also referred to as text data mining, roughly equivalent to text analytics, refers to t...
This thesis presents the application of various classification techniques on text documents. Since t...
Abstract Text Mining is a research area of retrieving high quality hidden information such as patter...
Abstract. We discuss a probabilistic graphical model that works for recognizing three types of text ...