---
abstract: |-
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.
altloc: []
chapter: ~
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confdates: July 29-August 3
conference: Proceedings of the 16th Annual International Joint Conference on Artificial Intelligence
confloc: 'Stockholm, Sweden'
contact_email: ~
creators_id: []
creators_name:
- family: Mayberry
given: Marshall R.
honourific: ''
lineage: ''
- family: Miikkulainen
given: Risto
honourific: ''
lineage: ''
date: 1999
date_type: published
datestamp: 2002-12-11
department: ~
dir: disk0/00/00/26/46
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editors_id: []
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eprint_status: archive
eprintid: 2646
fileinfo: /style/images/fileicons/application_pdf.png;/2646/1/mayberry.sardsrn.pdf
full_text_status: public
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keywords: SardSrn Shift-Reduce Parser
lastmod: 2011-03-11 08:55:07
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metadata_visibility: show
note: ~
number: ~
pagerange: 820-825
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refereed: TRUE
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relation_type: []
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reportno: ~
rev_number: 12
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status_changed: 2007-09-12 16:46:09
subjects:
- comp-sci-lang
- comp-sci-neural-nets
- comp-sci-art-intel
succeeds: ~
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title: 'SARDSRN: A NEURAL NETWORK SHIFT-REDUCE PARSER'
type: confpaper
userid: 2447
volume: ~