Abstract
One of the challenges in using statistical machine learning for text mining is coming up with the right set of text features. We have developed a system that uses genetic algorithms (GAs) to evaluate candidate feature sets to classify sentences in a document. We have applied this tool to find design rationale (the reasons behind design decisions) in two different datasets to evaluate our approach for finding rationale and to see how features might differ for the same classification target in different types of data. We used Chrome bug reports and transcripts of design sessions. We found that we were able to get results with less overfitting by using a smaller set of features common to the set optimized for each document type.
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Acknowledgements
We would like to thank Miami graduate students John Malloy and Jennifer Flowers for their work in annotating the SPSD data. The design sessions that produced the SPSD data were funded by the National Science Foundation (Award CCF-0845840). We would like to thank the workshop organizers, André van der Hoek, Marian Petre, and Alex Baker for granting access to the transcripts. We would also like to thank Dr. Mike Zmuda for suggesting we move the information gain calculation outside of the GA. This work was supported by NSF CAREER Award CCF-0844638 (Burge). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).
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Rogers, B., Justice, C., Mathur, T., Burge, J.E. (2017). Generalizability of Document Features for Identifying Rationale. In: Gero, J. (eds) Design Computing and Cognition '16. Springer, Cham. https://doi.org/10.1007/978-3-319-44989-0_34
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DOI: https://doi.org/10.1007/978-3-319-44989-0_34
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