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Research Papers: Design Informatics

Exploring Automated Text Classification to Improve Keyword Corpus Search Results for Bioinspired Design

[+] Author and Article Information
Michael W. Glier

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843

Daniel A. McAdams

Associate Professor
Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843

Julie S. Linsey

Assistant Professor
School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 9, 2013; final manuscript received July 25, 2014; published online October 8, 2014. Assoc. Editor: Ashok K. Goel.

J. Mech. Des 136(11), 111103 (Oct 08, 2014) (12 pages) Paper No: MD-13-1457; doi: 10.1115/1.4028167 History: Received October 09, 2013; Revised July 25, 2014

Bioinspired design is the adaptation of methods, strategies, or principles found in nature to solve engineering problems. One formalized approach to bioinspired solution seeking is the abstraction of the engineering problem into a functional need and then seeking solutions to this function using a keyword type search method on text based biological knowledge. These function keyword search approaches have shown potential for success, but as with many text based search methods, they produce a large number of results, many of little relevance to the problem in question. In this paper, we develop a method to train a computer to identify text passages more likely to suggest a solution to a human designer. The work presented examines the possibility of filtering biological keyword search results by using text mining algorithms to automatically identify which results are likely to be useful to a designer. The text mining algorithms are trained on a pair of surveys administered to human subjects to empirically identify a large number of sentences that are, or are not, helpful for idea generation. We develop and evaluate three text classification algorithms, namely, a Naïve Bayes (NB) classifier, a k nearest neighbors (kNN) classifier, and a support vector machine (SVM) classifier. Of these methods, the NB classifier generally had the best performance. Based on the analysis of 60 word stems, a NB classifier's precision is 0.87, recall is 0.52, and F score is 0.65. We find that word stem features that describe a physical action or process are correlated with helpful sentences. Similarly, we find biological jargon feature words are correlated with unhelpful sentences.

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Figures

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Fig. 1

A screen capture of the online survey delivered to participants

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Fig. 2

Histogram for the quality of passages from the corn shucker surveys

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Fig. 3

Precision, recall, and F score for NB classifiers using n features with the highest information gain

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Fig. 4

Precision, recall, and F score for kNN classifiers using n features with the highest information gain

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Fig. 5

Precision, recall, and F score for SVM classifiers using n features with the highest information gain

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Fig. 6

Number of high information gain features correlated with helpful and unhelpful sentences for several properties

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