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
Social Software is...
Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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