Abstract: In the general context of Knowledge Discovery, speci c techniques, called Text Mining techniques, are necessary to extract information from unstructured textual data. The extracted information can then be used for the classi cation of the content of large textual bases. In this paper, we present two examples of information that can be automatically extracted from text collections: probabilistic associations of key-words and prototypical document instances. The Natural Language Processing (NLP) tools necessary for such extractions are also presented.
KE (also referred to as knowledge discovery) techniques are used to extract information from unstructured data, which can be subsequently used for applying supervised or unsupervised learning techniques, such as clustering and classification of the content REF .
Text Mining - Knowledge extraction from unstructured textual data
6th Conference of International Federation of Classification Societies (IFCS-98
Computer Science