Tech Report

Text Mining Methods for Event Recognition in Stories

Navigating an online story collection requires a system which can make connections between the stories and their elements. One known way of accomplishing this is by annotating the stories, which can be a costly process. Finding methods for providing computer support for this process is a tactic for bringing the cost down. This paper describes several experiments which tested a variety of text mining methods for viability in accurately assisting the classification and annotation of stories in a small document collection. Latent Semantic Indexing is tested for dimension reduction, and decision trees, k nearest neighbor and naļve Bayes are all tested for classification. Additionally, both stemming and removing stopwords are tried. The study shows that all of these methods can be useful, but that there is great variability in their performance even within this small collection.

ID: kmi-05-02

Date: 2005

Author(s): Kelly Vincent

Resources:
Download PDF

View By

Other Publications

Latest Seminar
Prof Dene Grigar
Washington State University Vancouver

Electronic Literature: The challenges of born-digital fiction

Watch the live webcast

CONTACT US

Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support

COMMENT

If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team