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 on 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: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss th

SUBMITTER: Taira RK 

PROVIDER: S-EPMC10019629 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature