Using episodic memory in a memory based parser to assist machine reading

Kevin Livingston*, Christopher K Riesbeck

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


The central task for a Machine Reader is integrating information acquired from text with the machine's existing knowledge. Direct Memory Access Parsing (DMAP) is a machine reading approach that leverages existing knowledge and performs integration in the early stages of parsing natural language text. DMAP treats machine reading, fundamentally, as a task of knowledge recognition, and creates new knowledge structures and instances only when it cannot map the input text to existing knowledge. A goal of the research is to be able to use existing knowledge to improve the acquisition and integration of new knowledge, from (simplified) natural language. DMAP's understanding is driven by memory structures, and it maps immediately and directly to existing knowledge. This provides an opportunity to experiment with and evaluate methods for using existing knowledge (both semantic and episodic) to facilitate machine reading. We present the basic architecture of a DMAP implementation, three experiments to leverage existing episodic memory, and the implications of the experiments on future research.

Original languageEnglish (US)
Title of host publicationMachine Reading - Papers from the 2007 AAAI Spring Symposium, Technical Report
Number of pages6
StatePublished - 2007
Event2007 AAAI Spring Symposium - Stanford, CA, United States
Duration: Mar 26 2007Mar 28 2007

Publication series

NameAAAI Spring Symposium - Technical Report


Other2007 AAAI Spring Symposium
Country/TerritoryUnited States
CityStanford, CA

ASJC Scopus subject areas

  • General Engineering


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