A Finite and Real Time Processor for Natural Language

人们实时处理自然语言，短期记忆非常有限。这位研究人员正在开发一种计算架构，以模仿人类表现的这些非常理想的属性。句法组件在真实（线性）时间内处理句子，具有相当有限和固定的记忆要求。它似乎还将显著降低语法大小，并通过系统地清除无趣的歧义来简化语义。现在的挑战是证明这种新技术可以很好地扩展到自然语言语法的大片段。现在建议用两个已发布的测试套件的覆盖率来实现一个语法，报告关于语法大小和系统性能的结果。这将涉及扩展当前的模型来处理子分类、一致、连接和附加。另一个目标是在坚持有限资源假设的前提下，增加构建语义形式的能力。

People process natural language in real time and with very limited short-term memories. This researcher is developing a computational architecture that imitates these highly desirable attributes of human performance. The syntactic component processes sentences in real (linear) time, with quite limited and fixed memory requirements. It also appears that it will significantly hold down grammar size, and simplify semantics by systematically pruning away uninteresting ambiguities. The challenge now is to show that this new technique will scale up well for large fragments of natural language syntax. It is now proposed to implement a grammar with the coverage of two published test suites, reporting results with respect to grammar size and system performance. This will involve extending the current model to handle subcategorization, agreement, conjunction and adjunction. Another objective is to add the ability to build semantic forms compositionally, while holding to the finite resources hypothesis.