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Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data a nd on the discovery of interesting patterns within them. While most work o n KDD has been concerned with structured databases, there has been little wo rk on handling the huge amount of information that is available only in uns tructured textual form. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at t he term level. The mining process starts by preprocessing the document collec tion and extracting terms from the documents. Each document is then represent ed by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxon omy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995-1996.

Text mining at the term level