Text Mining at the Term Level

Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured 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 the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. 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 1995-19...

• Type: Conference
• Year: 1998
• Where: PKDD
• Authors: Ronen Feldman, Moshe Fresko, Yakkov Kinar, Yehuda Lindell, Orly Liphstat, Martin Rajman, Yonatan Schler, Oren Zamir
• Keywords: Data Mining, Documents, PKDD 1998, Text Mining, Unstructured Textual Form