Flackert, John C., Tait, John and Littlefair, Guy 2004, Scaling connectionist compositional representations, in Compositional Connectionism in Cognitive Science : 2004 AAAI Fall Symposium, AAAI Press, Menlo Park, Calif., pp. 20-24.
The Recursive Auto-Associative Memory (RAAM) has come to dominate connectionist investigations into representing compositional structure. Although an adequate model when dealing with limited data, the capacity of RAAM to scale-up to real-world tasks has been frequently questioned. RAAM networks are difficult to train (due to the moving target effect) and as such training times can be lengthy. Investigations into RAAM have produced many variants in an attempt to overcome such limitations. We outline how one such model ((S)RAAM) is able to quickly produce context-sensitive representations that may be used to aid a deterministic parsing process. By substituting a symbolic stack in an existing hybrid parser, we show that (S)RAAM is more than capable of encoding the real-world data sets employed. We conclude by suggesting that models such as (S)RAAM offer valuable insights into the features of connectionist compositional representations.
Language
eng
Field of Research
099999 Engineering not elsewhere classified
Socio Economic Objective
970109 Expanding Knowledge in Engineering
HERDC Research category
E2.1 Full written paper - non-refereed / Abstract reviewed
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