Parsing natural language into content for storage and retrieval in a content-addressable memory

Authors:
Roland Hausser
Affiliations:
Universität Erlangen-Nürnberg
Venue:
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Year:
2010

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Abstract

This paper explores the possibility of applying Database Semantic (DBS) to textual databases and the WWW. The DBS model of natural language communication is designed as an artificial cognitive agent with a hearer mode, a think mode, and a speaker mode. For the application at hand, the hearer mode is used for (i) parsing language data into sets of proplets, defined as nonrecursive feature structures, which are stored in a content-addressable memory called Word Bank, and (ii) for parsing the user query into a DBS schema employed for retrieval. The think mode is used to expand the primary data activated by the query schema to a wider range of relevant secondary and tertiary data. The speaker mode is used to realize the data retrieved in the natural language of the query.