Using a Sequential SOM to Parse Long-Term Dependencies (1999)
Simple Recurrent Networks (SRNs) have been widely used in natural language processing tasks. However, their ability to handle long-term dependencies between sentence constituents is somewhat limited. NARX networks have recently been shown to outperform SRNs by preserving past information in explicit delays from the network's prior output. However, it is unclear how the number of delays should be determined. In this study on a shift-reduce parsing task, we demonstrate that comparable performance can be derived more elegantly by using a SARDNET self-organizing map. The resulting architecture can represent arbitrarily long sequences and is cognitively more plausible.
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Citation:
In Proceedings of the 21st Annual Conference of the Cognitive Science Society, Martin Hahn and Scott C. Stoness (Eds.), pp. 367-372 1999. Hillsdale, NJ: Erlbaum.
Bibtex:

Marshall R. Mayberry III Ph.D. Alumni martym at coli dot uni-sb dot
Risto Miikkulainen Faculty risto [at] cs utexas edu