Graphical Models for Text: A New Paradigm for Text

Almost all text applications use the well known vector-space model for text representation and analysis. While the vector-space model has proven itself to be an effective and efficient representation for mining purposes, it does not preserve information about the order-ing of the words in the representation. In this paper, we will intro-duce the concept of distance graph representations of text data. Such representations preserve distance and ordering information between the words, and provide a much richer representation of the underlying text. This approach enables knowledge discovery from text which is not possible with the use of a pure vector-space representation, because it loses much less information about the or-dering of the underlying words. Furthermore, this representation does not require the development of new mining and management techniques. This is because the technique can also be converted into a structural version of the vector-space representation, which allows the use of all existing tools for text. In addition, existing techniques for graph and XML data can be directly leveraged with this new representation. Thus, a much wider spectrum of algo-rithms is available for processing this representation