Automatic detection of significant features and event timeline construction from temporally tagged data

Date

2009-08-14T19:34:41Z

Journal Title

Journal ISSN

Volume Title

Publisher

Kansas State University

Abstract

The goal of my project is to summarize large volumes of data and help users to visualize how events have unfolded over time. I address the problem of extracting overview terms from a time-tagged corpus of data and discuss some previous work conducted in this area. I use a statistical approach to automatically extract key terms, form groupings of related terms, and display the resultant groups on a timeline. I use a static corpus composed of news stories, as opposed to an on-line setting where continual additions to the corpus are being made. Terms are extracted using a Named Entity Recognizer, and importance of a term is determined using the [superscript]X[superscript]2 measure. My approach does not address the problem of associating time and date stamps with data, and is restricted to corpora that been explicitly tagged. The quality of results obtained is gauged subjectively and objectively by measuring the degree to which events known to exist in the corpus were identified by the system.

Description

Keywords

Timeline generation, Feature extraction

Graduation Month

August

Degree

Master of Science

Department

Department of Computing and Information Sciences

Major Professor

William H. Hsu

Date

2009

Type

Report

Citation