Natural Language Processing (NLP) is concerned with processing ordinary, unrestricted text. This work takes a new approach to a traditional NLP task, using neural computing methods. A parser which has been successfully implemented is described. It is a hybrid system, in which neural processors operate within a rule based framework. The neural processing components belong to the class of Generalized Single Layer Networks (GSLN). In general, supervised, feed-forward networks need more than one layer to process data. However, in some cases data can be pre-processed with a non-linear transformation, and then presented in a linearly separable form for subsequent processing by a single layer net. Such networks o er advantages of functional transparency and operational speed. For our parser, the initial stage of processing maps linguistic data onto a higher order representation, which can then be analysed by a single layer network. This transformation is supported by information theoretic analysis. Three di erent algorithms for the neural component were investigated. Single layer nets can be trained by nding weight adjustments based on (a) factors proportional to the input, as in the Perceptron, (b) factors proportional to the existing weights, and (c) an error minimization method. In our experiments generalization ability varies little � method (b) is used for a prototype parser. This is available via telnet.

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The pattern matching capabilities of neural networks can be mobilised for an automated, natural language, partial parser. First, language complexity is addressed by decomposing the problem into more tractable subtasks. Second, a representation is devised that enables effective, single layer networks to be used to map a pre-defined grammatic framework onto actual sentences. This paper examines data representation, network architecture and learning algorithms appropriate for linguistic data with their characteristic distributions. Users can access a working prototype via telnet on which they can try their own text. 1

The complexity of parsing English sentences can be reduced by decomposing the problem into three subtasks. Declarative sentences can almost always be segmented into three concatenated sections: pre-subject - subject - predicate. Other constituents, such as clauses, phrases, noun groups, are contained within these segments, but do not normally cross the boundaries between them. Though a constituent in one section may have dependent links to elements in other sections, such as agreement between the head of the subject and the main verb, once the three sections have been located, they can then be partially processed separately, in parallel. An information theoretic analysis is used to support this approach. If sentences are represented as sequences of part-of-speech tags, then modelling them with the tripartite segmentation reduces the entropy. This indicates that some of the structure of the sentence has been captured. The tripartite segmentation can be produced automatically, using the ...

The pattern matching capabilities of neural networks can be used to locate syntactic constituents of natural language. This paper describes a fully automated hybrid system, using neural nets operating within a grammatic framework. It addresses the representation of language for connectionist processing, and describes methods of constraining the problem size. The function of the network is briefly explained, and results are given. 1 Introduction The pattern matching capabilities of neural networks can be used to detect syntactic constituents of natural language. This approach bears comparison with probabilistic systems, but has the advantage that negative as well as positive information can be modelled. Also, most computation is done in advance, when the nets are trained, so the run time computational load is low. In this work neural networks are used as part of a fully automated system that finds a partial parse of declarative sentences. The connectionist processors operat...

The pattern matching capabilities of neural networks can be used to loc- ate syntactic constituents of natural language.