Date of Award

1-1-2009

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Computer Science

Content Description

1 online resource (vii, 130 pages) : illustrations (some color)

Dissertation/Thesis Chair

Tomek Strzalkowski

Committee Members

George Berg, Andraw Haas

Keywords

event extraction, machine learning, semi-supervised, Computational learning theory, Computational linguistics, Data mining, Speech processing systems

Subject Categories

Computer Sciences

Abstract

Information Extraction (IE) is a technique for automatically extracting structured data from text documents. One of the key analytical tasks is extraction of important and relevant information from textual sources. While information is plentiful and readily available, from the Internet, news services, media, etc., extracting the critical nuggets that matter to business or to national security is a cognitively demanding and time consuming task. Intelligence and business analysts spend many hours poring over endless streams of text documents pulling out reference to entities of interest (people, locations, organizations) as well as their relationships as reported in text. Such extracted "information nuggets" are then entered into a structured database for further analysis that may expose various trends or hidden relationships.

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