3.  ODM provides infrastructure for developing data mining applications suitable for addressing a variety of business problems involving text. Among these, the following specific technologies provide key elements for addressing problems that require text mining :

4.  A large number of document classification applications fall into one of the following:  Assigning multiple labels to a document. ODM does not support this case.  Assigning a document to one of many labels..

5.  Clustering is used frequently in text mining; the main applications of clustering in text mining are  Taxonomy generation  Topic extraction  Grouping the hits returned by a search engine

6. There are two kinds of text mining problems for which feature extraction is useful:  Extract features from actual text. Oracle Text is designed to solve this kind of problem.  Extract semantic features or higher-level features from the basic features uncovered when features are extracted from actual text.

8.  Association models can be used to uncover the semantic meaning of words. For example, suppose that the word sheep co-occurs with words like sleep, fence, chew, grass, meadow, farmer, and shear. An association model would include rules connecting sheep with these concepts.

9.  Text mining methods and software is also being researched and developed by major firms, including IBM and Microsoft, to further automate the mining and analysis processes, and by different firms working in the area of search.IBMMicrosoft

10.  Text mining is being used by large media companies, such as the Tribune Company, to clarify information and to provide readers with greater search experiences, which in turn increases site "stickiness" and revenue. Tribune Company