A Semi-Supervised Approach for Identification of the Sections in Charge of RFQ Documents
Hidetaka Izumo, Yiou Wang · 2019
Identification of sections in charge of a request for quotation (RFQ), a type of business-specific document that seeks an itemized list of prices for a product or service, is usually performed manually and is very time-consuming, especially in the manufacturing industry. This study presents a simple semi-supervised classification approach for automatic section identification of RFQ documents. We conceive the identification task as text classification task for different sections and introduce novel features derived from unlabeled data to enhance the performance. We evaluate the usefulness of our approach in a series of experiments on a collection of RFQ documents in the actual business operations and obtain satisfactory results for most test collections.