Parsing Neural Networks Combining Symbolic and Connectionist Approaches

In this paper we suggest combining symbolic and subsymbolic approaches in order to build fast parsers based on context-free grammars. Symbol-based parsers well known in Artificial Intelligence (AI) and Computational Linguistics (CL) provide highly developed tools and techniques, but they suffer from certain inabilities, for example to process ambiguous sentences or ungrammatical structures.Connectionist parsers, on the other hand, have problems with representing recursive structures, processing sequences, and the handling of variables. But they have the advantage of being fault-tolerant and representing syntactic and semantic knowledge in a distributed manner.We analyzed the existing work on connectionist parsers and developed three different systems (PAPADEUS, INKAS, and INKOPA) in order to tackle the above described problems of symbolic and connectionist approaches. The main common characteristic of all three systems is the dynamic generation of the parse tree and thus of the parsing network. This technique was developed using the known parsing techniques in AI and CL, especially chart-parsing. Also the use of context-free grammars had its source in these fields.

ICSI Technical Report TR-94-021

Technical Report