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Resolving References and Identifying Existing Knowledge in a Memory Based Parser

机译：解决基于存储器的解析器中的引用并识别现有知识

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摘要

Learning by reading systems, designed to acquire episodic (instance based) knowledge, ultimately have to integrate that knowledge into an underlying memory. In order to effectively integrate new knowledge with existing knowledge such a system needs to be able to resolve references to the instances (agents, locations, events, etc.) it is reading about with those already existing in memory. This is necessary to extend existing memory structures, and to avoid incorrectly producing duplicate memories. Direct Memory Access Parsing (DMAP) leverages existing knowledge and performs reference resolution and memory integration in the early stages of parsing natural language text. By performing incremental memory integration our system can reduce the number of ambiguous sentence interpretations and coreference mappings it will explore in-depth, however this savings is currently canceled out by the run-time cost of reference resolution algorithm. This paper supports the continued investigation of this line of research, which is to identify and evaluate the extent to which semantic and episodic memory can facilitate natural language understanding, especially when used early in the language understanding process.
机译：通过阅读系统学习，旨在获取eCisodic（基于实例）知识，最终必须将该知识集成到底层内存中。为了有效地将新知识与现有知识集成，这样的系统需要能够解决它正在读取的实例（代理，位置，事件等）的引用。这是扩展现有内存结构的必要条件，避免不正确的产生重复记忆。直接内存访问解析（DMAP）利用现有知识并在解析自然语言文本的早期阶段执行参考分辨率和内存集成。通过执行增量内存集成，我们的系统可以减少模糊的句子解释和Coreference映射的数量，它将深入探讨，但是当前通过参考分辨率算法的运行时间成本取消了此节省。本文支持持续调查这一研究线，即识别和评估语义和插曲记忆可以促进自然语言理解的程度，特别是当在语言理解过程中使用时。

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