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

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 transfor...

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Bibliographic Details
Published in:Journal of global information management 2022-01, Vol.30 (1), p.1-26
Main Authors: Chang, Ai-Che, Trappey, Amy J. C, Trappey, Charles V, Chien, Jack Y. C. Chang
Format: Article
Language:English
Subjects:
Algorithms
Bids
Computational linguistics
Costs
Data mining
Design specifications
Electric transformers
Information management
Language
Language processing
Linguistics
Machine learning
Manufacturers
Manufacturing
Methods
Natural language interfaces
Natural language processing
Product design
Profit margins
Risk reduction
Semantics
Sentences
Specifications
Statistical analysis
Vector space
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Description
Summary: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.
ISSN:1062-7375
1533-7995
DOI:10.4018/JGIM.309082