Tech Reports
Tech Report kmi-05-02 Abstract
Text Mining Methods for Event Recognition in Stories
Techreport ID: kmi-05-02
Date: 2005
Author(s): Kelly Vincent
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 naïve 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.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Future Internet is...

To succeed the Future Internet will need to address a number of cross-cutting challenges including:
- Scalability in the face of peer-to-peer traffic, decentralisation, and increased openness
- Trust when government, medical, financial, personal data are increasingly trusted to the cloud, and middleware will increasingly use dynamic service selection
- Interoperability of semantic data and metadata, and of services which will be dynamically orchestrated
- Pervasive usability for users of mobile devices, different languages, cultures and physical abilities
- Mobility for users who expect a seamless experience across spaces, devices, and velocities
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