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Comparing Extant Story Classifiers: Results & New Directions

Authors Joshua D. Eisenberg, W. Victor H. Yarlott, Mark A. Finlayson

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  • Story Detection
  • Machine Learning
  • Natural Language Processing
  • Perceptron Learning

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Abstract

Having access to a large set of stories is a necessary first step for robust and wide-ranging computational narrative modeling; happily, language data - including stories - are increasingly available in electronic form. Unhappily, the process of automatically separating stories from other forms of written discourse is not straightforward, and has resulted in a data collection bottleneck. Therefore researchers have sought to develop reliable, robust automatic algorithms for identifying story text mixed with other non-story text. In this paper we report on the reimplementation and experimental comparison of the two approaches to this task: Gordon's unigram classifier, and Corman's semantic triplet classifier. We cross-analyze their performance on both Gordon's and Corman's corpora, and discuss similarities, differences, and gaps in the performance of these classifiers, and point the way forward to improving their approaches.

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Joshua D. Eisenberg, W. Victor H. Yarlott, and Mark A. Finlayson. Comparing Extant Story Classifiers: Results & New Directions. In 7th Workshop on Computational Models of Narrative (CMN 2016). Open Access Series in Informatics (OASIcs), Volume 53, pp. 6:1-6:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016) https://doi.org/10.4230/OASIcs.CMN.2016.6

Author Details

Joshua D. Eisenberg
W. Victor H. Yarlott
Mark A. Finlayson

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