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.

探讨了一种医学自然语言处理系统，其框架受到人类认知机制的启示，该系统通过一种分层语义组成模型（HSCM）来指导解释过程，其中包括语义记忆、语义组合、语义激活和分层预测编码等认知方面。为了将自由文本句子转换为其意义的逻辑表示，研究设计了一种生成语义模型和一个相关的语义解析器。