A Natural Language Understanding Approach Toward Extraction of Specifications from Request for Proposals

Abstract

Industry 4.0 has witnessed a widespread use of Artificial Intelligence (AI), which, however, often focuses on the operational aspects. In contrast, the life-cycle of any industrial project begins much earlier. Motivated by this, we present an intent-based approach toward bid engineering. In particular, we consider the use of AI to automatically extract the intended specifications-technical and non-technical-of customers from Requests for Proposals (RFPs) by defining relevant data models. Subsequently, we annotate texts from real-life RFPs to train an AI model. In addition, we also design RfpAnno, an end-to-end solution to annotate documents, train models, and extract specifications as structured data. Experimental results indicate that the AI model has about 85% precision and recall, on average, using the test data set. Overall, RfpAnno can potentially reduce the time and effort required by bid engineers to manually copy requirements from RFPs.