Kevin Livingston and Christopher K. Riesbeck
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.