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Subsymbolic Parsing of Sequences: The SARDSRN model
Active from 1998 - 1999
The SARDSRN model demonstrates how the performance of a Simple Recurrent Network (SRN) parser can be enhanced significantly by the addition of a SARDNET module. SARDNET is an extension to the Self-Organizing Map architecture (SOM) which allows self-organization of sequences. The SARDNET module in the SARDSRN parser explicitly represents the input sequence in a self-organizing map. The distributed SRN component leads to good generalization and robust cognitive properties, whereas the SARDNET map provides exact representations of the sentence constituents. This combination allows SARDSRN to learn to parse sentences with more complicated structure than can the SRN alone.
People
Marshall R. Mayberry III
Ph.D. Alumni
marty mayberry [at] gmail com
Hong Ming Yeh
Formerly affiliated Ph.D. Student
hongming [at] cs utexas edu
Publications
SARDSRN: A Neural Network Shift-Reduce Parser
1999
Marshall R. Mayberry III and Risto Miikkulainen, In
Proceedings of the 16th Annual International Joint Conference on Artificial Intelligence (IJCAI-99)
, pp. 820-825, Stockholm, Sweden 1999. San Francisco, CA: Kaufmann.
Related Areas
Natural Language Processing (Cognitive)
Software/Data
MIR Sentence Processing Package
The MIR Sentence Processing package contains the C source code for the MIR system, as well as a selection of scripts wi...
1998
Labs
Neural Networks