1 research outputs found
Design considerations for a hierarchical semantic compositional framework for medical natural language understanding
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