Abstract #302321

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JSM 2003 Abstract #302321
Activity Number: 208
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #302321
Title: Document Classification: Topic Spotting Using Geodesics
Author(s): Christian Posse*+ and Igor Perisic
Companies: Pacific Northwest National Laboratory and Entopia
Address: 2258 Morency Dr., Richland, WA, 99352-9517,
Keywords:
Abstract:

In information retrieval, Latent Semantic Indexing (LSI) satisfactorily addresses the problems of synonymy and polysemy. However, when it comes to topic spotting (i.e., identifying which of a set of predefined topics are present in a document) the classification performance of global LSI worsens as topic frequency decreases. This is due to the fact that infrequent topics are usually indicated by infrequent terms that are likely to be discarded by the LSI representation. On the other hand, topic-based LSI's are quite unreliable due to the generally small amount of available information for each topic. We propose a geometric approach that relies on geodesics to efficiently combine the best of both LSI worlds (local and global).


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