No abstract available.

This paper describes a combination of compression methods which may be used to reduce the size of inverted indexes for very large text databases. These methods are Prefix Omission, Run-Length Encoding, and a novel family of numeric representations called n-s coding. Using these compression methods on two different text sources (the King James Version of the Bible and a sample of Wall Street Journal Stories), the compressed index occupies less than 40% of the size of the original text, even when both stopwords and numbers are included in the index. The decreased time required for I/O can almost fully compensate for the time needed to uncompress the postings. This research is part of an effort to handle very large text databases on the CM-5, a massively parallel MIMD supercomputer.

We describe a method for classifying news stories using Memory Based Reasoning (MBR) a k-nearest neighbor method), that does not require manual topic definitions. Using an already coded training database of about 50,000 stories from the Dow Jones Press Release News Wire, and SEEKER [Stanfill] (a text retrieval system that supports relevance feedback) as the underlying match engine, codes are assigned to new, unseen stories with a recall of about 80% and precision of about 70%. There are about 350 different codes to be assigned. Using a massively parallel supercomputer, we leverage the information already contained in the thousands of coded stories and are able to code a story in about 2 seconds. Given SEEKER, the text retrieval system, we achieved these results in about two person-months. We believe this approach is effective in reducing the development time to implement classification systems involving large number of topics for the purpose of classification, message routing etc.