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Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques

Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques

Amy J. C. Trappey, Ai-Che Chang, Charles V. Trappey, Jack Y. C. Chang Chien
Copyright: © 2022 |Volume: 30 |Issue: 1 |Pages: 26
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799893233|DOI: 10.4018/JGIM.309082
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MLA

Trappey, Amy J. C., et al. "Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques." JGIM vol.30, no.1 2022: pp.1-26. http://doi.org/10.4018/JGIM.309082

APA

Trappey, A. J., Chang, A., Trappey, C. V., & Chien, J. Y. (2022). Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques. Journal of Global Information Management (JGIM), 30(1), 1-26. http://doi.org/10.4018/JGIM.309082

Chicago

Trappey, Amy J. C., et al. "Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques," Journal of Global Information Management (JGIM) 30, no.1: 1-26. http://doi.org/10.4018/JGIM.309082

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Abstract

Request for quotation (RFQ) is a lengthy document soliciting vendor products and services according to rigid specifications. This research develops an integrated natural language processing (NLP), text mining, and machine learning approach for intelligent RFQ summarization. Over 1,300 power transformer RFQ requests are used to build a word-embedding model for training and testing. Domain keywords are extracted using N-gram TF-IDF. The method automatically extracts essential specifications such as voltage, capacity, and impedance from RFQs using text analytics. The K-means algorithm groups the sentences of each specification. The TextRank algorithm identifies important sentences of all specifications to generate RFQ summaries. The summarization system helps engineers shorten the time to identify all specifications and reduces the risk of missing important requirements during manual RFQ reading. The system helps improve the complex product design for manufacturers and improve the cost estimation and competitiveness of quotations in a highly competitive marketplace.