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SARDSRN: A Neural Network Shift-Reduce Parser (1999)
Marshall R. Mayberry III
and
Risto Miikkulainen
Simple Recurrent Networks (SRNs) have been widely used in natural language tasks. SARDSRN extends the SRN by explicitly representing the input sequence in a SARDNET 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, and suggests that the approach could scale up to realistic natural language.
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Citation:
In
Proceedings of the 16th Annual International Joint Conference on Artificial Intelligence (IJCAI-99)
, 820-825, Stockholm, Sweden, 1999. San Francisco, CA: Kaufmann.
Bibtex:
@InProceedings{mayberry:ijcai99, title={SARDSRN: A Neural Network Shift-Reduce Parser}, author={Marshall R. Mayberry III and Risto Miikkulainen}, booktitle={Proceedings of the 16th Annual International Joint Conference on Artificial Intelligence (IJCAI-99)}, address={Stockholm, Sweden}, publisher={San Francisco, CA: Kaufmann}, pages={820-825}, url="http://nn.cs.utexas.edu/?mayberry:ijcai99", year={1999} }
People
Marshall R. Mayberry III
Ph.D. Alumni
marty mayberry [at] gmail com
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Projects
Subsymbolic Parsing of Sequences: The SARDSRN model
1998 - 1999
Areas of Interest
Natural Language Processing (Cognitive)
Cognitive Science