## 1 Introduction  
Automated document understanding is commonly used to improve web searches and document content indexing [1]. However, information and data within a document can be represented in a variety of forms such as a paragraph of text, a table, or an image. Further, one document can contain multiple tables, images, and paragraphs of text, and the same document can contain different representations of the same content. On a much larger scale, a set of documents may contain similar information; however, their data representations may be very different. With so many ways to represent information, the automated task of document content extraction and representation becomes problematic.  
Additionally, differences in document layout make it difficult to automatically extract relationships within the data. In the same corpus, opposing layouts complicate the ability to detect relationships between different documents. Moreover, conflicting document layouts make it difficult to aggregate relevant data within the same document. However, the use of standard data formats greatly facilitates finding and organizing the most relevant data in a document [2].  
This work is part of an ongoing collaboration between the University of Tennessee and Oak Ridge National Laboratory to study the use of visual gaze tracking to analyze document layout. The collaboration aims to apply gaze tracking analysis to note which document objects a person observes and the order in which these objects are observed. A _document object_ is defined to be any element of a document such as a paragraph, image, table, _etc._ The data collected from the gaze tracking analysis helps to identify which objects in the document are of the most importance as well as the relationships among the objects. An object's importance can then be determined by analyzing the amount of time that a person spends looking at an object while data relationships can be determined by noting the order in which the person views objects in the document, respectively.  
Another collaborative goal is to aggregate document content into objects. The generated collection of objects will establish a new set of metadata that describes each document, such as the aggregation shown in Figure 1. Each object in the metadata will contain a _tag_ describing what the object is and how it is related to other objects in the document. Once the metadata is created, it can be used to identify documents that contain similar data. Further, the metadata can assist with searching within the document set and generating summaries of the data contained within the corpus.  
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{142.3 pt}|} \hline
**Year** & **Historical Events Contributing to Development of Seamless Knitting** \\ \hline
1589 & **[PERSON] in England Inverted the first flat-bed frame to create hosley.** \\ \hline
1863 & [PERSON] inverted the first operational V-bed flat \\ \hline \end{tabular}
\end{table}
Table 1: Historical Events Contributing to Development of Seamless KnittingWith the above goals in mind, we have recently created ground truth metadata for United States patents. After rigorous preprocessing using techniques of data exploration and text mining, we have created an XML data format to describe content found in the patent documentation. We restricted our study of document clustering to documents that contain table objects in order to assess the value (with respect to content) of tabulated data in the associated HTML files.  
Our procedures were implemented on document sets with and without numerical words. By _numerical word_ we refer to any element in the patent document which contains at least one number. The words _1988_, _1a_, and _010111 January_ are examples of numerical words. The inclusion or exclusion of numerical words from a document set's dictionary is applied to determine if such words contribute a measurable value to a document's content. A methodology to evaluate which indexing model best clusters subsets of patent documents was also developed.