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Grammatical analysis of Hindi sentences using connectionist approach
Author(s) -
Prakash Nupur,
Garg Kumkum
Publication year - 1998
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00074
Subject(s) - computer science , parsing , artificial intelligence , natural language processing , parser combinator , connectionism , artificial neural network , sentence , perceptron , top down parsing , speech recognition
This paper presents a simple connectionist approach to parsing of a subset of sentences in the Hindi language, using Rule based Connectionist Networks (RBCN) as suggested by Fu in 1993. The basic grammar rules representing Kernel Hindi sentences have been used to determine the initial topology of the RBCN. The RBCN is based on a multilayer perceptron, trained using the backpropagation algorithm. The terminal symbols defined in the language structure are mapped onto the input nodes, the non‐terminals onto hidden nodes and the start symbol onto the single output node of the network structure. The training instances are sentences of arbitrary, but fixed maximum length and fixed word order.A neural network based recognizer is used to perform grammaticality determination and parse tree generation of a given sentence. The network is exposed to both positive and negative training instances, derived from a simple context‐free‐grammar (CFG), during the training phase. The trained network recognizes seen sentences (sentences present in the training set) with 98–100% accuracy. Since a neural net based recognizer is trainable in nature, it can be trained to recognize any other CFG, simply by changing the training set. This results in reducing programming effort involved in parser development, as compared to that of the conventional AI approach. The parsing time is also reduced to a great extent as compared to that of a conventional parser, as a result of the inherent parallelism exhibited by neural net architecture.