Creating and exploiting aggregate information from text using a novel Categorized Document Base abstract

9.SUMMARY

When setting up the CDB, it is important that category descriptors are unambiguous and sufficient highly relevant documents exist for even obscure categories in the classification scheme. The aggregate statistics are independently useful, but can also be integrated with structured data for the categories – for example, using bubble charts or tables, and using category identifiers to cross-reference from the aggregate statistics for each document category to the traditional structured numeric data.

We assessed the reasonableness of our CDB approach through a number of experiments that compared our aggregate results for each category to closely related numeric data, to determine whether our proxy measures – aggregates derived from textual data – correlate at all with their quantitative counterparts obtained from well-accepted structured sources. Our experiments seem to indicate that, for a taxonomy such as the US GSA GLC list of US locations, where the CDB can be mechanically populated with relevant content for each category, the CDB appears to produce a plausible reflection of both natural and market phenomena in multiple industries, but only in industries where the locations under comparison diverge substantially. The results, therefore, appear to partially support the hypothesis at the start of this paper that our CDBs can allow mountains of text information on locations to be distilled into sensible comparisons of those locations.

We described some applications of our research, including market research, sales lead prospecting, and rapidly obtaining insights into new collections of topics or items. We also briefly documented a number of limitations we have found in the CDB population and exploration process – helpful repairs and alterations, which improve the quality of the results are outside of the scope of this paper and will be discussed in detail in future work.

10.ACKNOWLEDGMENTS

Our thanks to the following research assistants, who assisted with the implementation and evaluation of the features and algorithms described in the main text:

• 
  taxonomy importation scripts: Jason Gurwin, Debbie Chiou.
  
• 
  population routines: Joseph Leary, Ryan Mark Fleming, Shawn Zhou.
  
• 
  results visualization features: David Gorski, Ryan Namdar, Adam Altman, Ankit Choudari, Michael Pan, Myron Robinson, Mark Weinberger.
  
• 
  experimental evaluation: Shawn Zhou, Ava Zhiyang Yang, Aditya Mehrotra, Peng Chen, Anjay Kumar, Anjay Aushij, Erik Malmgren-Samuel.
  

Thanks are also due to:

• 
  John Ranieri and Ray Miller, of Du Pont Corporation ( http://www.dupont.com/ ), for championing CDB experiments within Du Pont, and providing feedback on the results.
  
• 
  Chris and Natasha Ashton, of PetPlan USA pet insurance ( http://www.gopetplan.com/ ), for the provision of PetPlan’s dog health insurance sales data.
  
• 
  The reviewers, whose suggestions were very valuable in improving the content of this paper.
  

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