Algorithms 2012, 5(4), 490-505; doi:10.3390/a5040490
Article

The Effects of Tabular-Based Content Extraction on Patent Document Clustering

1 EECS Department, Min H. Kao Building Suite 401, University of Tennessee, 1520 Middle Drive, Knoxville, TN 37996, USA 2 Catalyst Repository Systems, 1860 Blake Street, 7th Floor, Denver, CO 80202, USA
* Author to whom correspondence should be addressed.
Received: 1 July 2012; in revised form: 16 August 2012 / Accepted: 9 October 2012 / Published: 22 October 2012
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Abstract: Data can be represented in many different ways within a particular document or set of documents. Hence, attempts to automatically process the relationships between documents or determine the relevance of certain document objects can be problematic. In this study, we have developed software to automatically catalog objects contained in HTML files for patents granted by the United States Patent and Trademark Office (USPTO). Once these objects are recognized, the software creates metadata that assigns a data type to each document object. Such metadata can be easily processed and analyzed for subsequent text mining tasks. Specifically, document similarity and clustering techniques were applied to a subset of the USPTO document collection. Although our preliminary results demonstrate that tables and numerical data do not provide quantifiable value to a document’s content, the stage for future work in measuring the importance of document objects within a large corpus has been set.
Keywords: text mining; patent documents; table data

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MDPI and ACS Style

Koessler, D.R.; Martin, B.W.; Kiefer, B.E.; Berry, M.W. The Effects of Tabular-Based Content Extraction on Patent Document Clustering. Algorithms 2012, 5, 490-505.

AMA Style

Koessler DR, Martin BW, Kiefer BE, Berry MW. The Effects of Tabular-Based Content Extraction on Patent Document Clustering. Algorithms. 2012; 5(4):490-505.

Chicago/Turabian Style

Koessler, Denise R.; Martin, Benjamin W.; Kiefer, Bruce E.; Berry, Michael W. 2012. "The Effects of Tabular-Based Content Extraction on Patent Document Clustering." Algorithms 5, no. 4: 490-505.

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