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DOI: 10.4230/OASIcs.CMN.2016.6
URN: urn:nbn:de:0030-drops-67079
URL: https://drops.dagstuhl.de/opus/volltexte/2016/6707/
Eisenberg, Joshua D. ; Yarlott, W. Victor H. ; Finlayson, Mark A.
Comparing Extant Story Classifiers: Results & New Directions
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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.
BibTeX - Entry
@InProceedings{eisenberg_et_al:OASIcs:2016:6707,
author = {Joshua D. Eisenberg and W. Victor H. Yarlott and Mark A. Finlayson},
title = {{Comparing Extant Story Classifiers: Results & New Directions}},
booktitle = {7th Workshop on Computational Models of Narrative (CMN 2016)},
pages = {6:1--6:10},
series = {OpenAccess Series in Informatics (OASIcs)},
ISBN = {978-3-95977-020-0},
ISSN = {2190-6807},
year = {2016},
volume = {53},
editor = {Ben Miller and Antonio Lieto and R{\'e}mi Ronfard and Stephen G. Ware and Mark A. Finlayson},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2016/6707},
URN = {urn:nbn:de:0030-drops-67079},
doi = {10.4230/OASIcs.CMN.2016.6},
annote = {Keywords: Story Detection, Machine Learning, Natural Language Processing, Perceptron Learning}
}
| Keywords: | Story Detection, Machine Learning, Natural Language Processing, Perceptron Learning | |
| Seminar: | 7th Workshop on Computational Models of Narrative (CMN 2016) | |
| Issue date: | 2016 | |
| Date of publication: | 25.10.2016 |
