Table Understanding for Information Retrieval

This thesis proposes a novel approach for finding tables in text files containing a mixture of unstructured and structured text. Tables may be arbitrarily complex because the data in the tables may themselves be tables and because the grouping of data elements displayed in a table may be very complex. Although investigators have proposed competence models to explain the structure of tables, there are no computationally feasible performance models for detecting and parsing general structures in real data. Our emphasis is placed on the investigation of a new statistical procedure for detecting basic tables in plain text documents. The main task here is defining and testing this theory in the context of the Odessa Digital Library.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2002

Author dc:creator

• Pande, Ashwini K.

Chair dc:contributor.committeechair

• Ehrich, Roger W.

Committee members dc:contributor.committeemember

• Fox, Edward A.
• North, Christopher L.

• Information retrieval
• Statistical crosscorrelation
• Odessa digital library
• detection heuristics
• Table detection

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-08282002-151909

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/34820