Text Classification Using Graph-Encoded Linguistic Elements

Kevin R. Gee and Diane J. Cook, The University of Texas at Arlington

Inspired by the goal to more accurately classify text, we describe an effort to map tokens and their characteristic linguistic elements into a graph and use that expressive representation to classify text phrases. We outperform the bag-of-words approach by exploiting word order and the semantic and syntactic characteristics within the phases. In this study, we map tagged corpora into a placeholder graph structure and classify the phrases within, using the cross-dimensional linguistic characteristics of each token. Finally, we present heuristics for use in applying this method to other corpora.


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