Entity categorization over large document collections

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Entity categorization over large document collections

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International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Las Vegas, Nevada, USA

SESSION: Research papers table of contents

Pages 274-282

Year of Publication: 2008

ISBN:978-1-60558-193-4

Authors

Venkatesh Ganti	Microsoft Research, Redmond, WA, USA
Arnd C. König	Microsoft Research, Redmond, WA, USA
Rares Vernica	University of California, Irvine, Irvine, CA, USA

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 19, Downloads (12 Months): 291, Citation Count: 1

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ABSTRACT

Extracting entities (such as people, movies) from documents and identifying the categories (such as painter, writer) they belong to enable structured querying and data analysis over unstructured document collections. In this paper, we focus on the problem of categorizing extracted entities. Most prior approaches developed for this task only analyzed the local document context within which entities occur. In this paper, we significantly improve the accuracy of entity categorization by (i) considering an entity's context across multiple documents containing it, and (ii) exploiting existing large lists of related entities (e.g., lists of actors, directors, books). These approaches introduce computational challenges because (a) the context of entities has to be aggregated across several documents and (b) the lists of related entities may be very large. We develop techniques to address these challenges. We present a thorough experimental study on real data sets that demonstrates the increase in accuracy and the scalability of our approaches.

REFERENCES

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