Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Abstract

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers about a hierarchical semantic compositional model (HSCM) which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects including semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.

References in corpus (6)

• On the Opportunities and Risks of Foundation Models
• The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence
• Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
• Neurosymbolic AI: The 3rd Wave
• Emergence of Separable Manifolds in Deep Language Representations
• On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies