A Semi-Supervised Information Extraction Framework for Large Redundant Corpora

The vast majority of text freely available on the Internet is not available in a form that computers can understand. There have been numerous approaches to automatically extract information from human- readable sources. The most successful attempts rely on vast training sets of data. Others have succeeded in extracting restricted subsets of the available information. These approaches have limited use and require domain knowledge to be coded into the application. The current thesis proposes a novel framework for Information Extraction. From large sets of documents, the system develops statistical models of the data the user wishes to query which generally avoid the lim- itations and complexity of most Information Extractions systems. The framework uses a semi-supervised approach to minimize human input. It also eliminates the need for external Named Entity Recognition systems by relying on freely available databases. The final result is a query-answering system which extracts information from large corpora with a high degree of accuracy.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2008

Author dc:creator

• Normand, Eric

Contributors dc:contributor

• Abdelguerfi, Mahdi
• Richard III, Golden
• Tu, Shengru

• Information Extraction
• Natural Language Processing
• Support Vector Machine
• Machine Learn- ing
• Information Retrieval
• unstructured text

Repository record dc:identifier

https://scholarworks.uno.edu/td/877

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1857