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Vyas, Prafulla
- Natural Language Words Analysis for Affective Scene Generation from Written Text Using Artificial Neural Network
Authors
1 Raipur Institute of Technology, Madir Hasod, Raipur, IN
2 Pt. Ravishankar Shukla University, Raipur (C.G.), IN
Source
Research Journal of Engineering and Technology, Vol 3, No 1 (2012), Pagination: 1-5Abstract
This paper presents an artificial neural network approach to word analysis to generate the 3 dimension scene or image from the textual description. We start with the recognition of characters and then form the words from these characters. The words used in natural language will have some special meaning and gives some information. Each word represents some inherited properties of some of the objects. The properties of each word will depend on the object being used in the sentence. Therefore the word itself gives lots of information about the objects. The neural network approach to lexical classifications is the first step to find the objects and its properties. The next step is neural network based approach to word classification is extracting words attribute and then relating it with the other words using artificial neural network. The multilayer feedforward neural network will be used. Here we will analyze the different parts of the speech with their inherit properties which the word have in the sentence.
A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in language learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent studies that examined the brain processes involved in learning non-linguistic sequences, or artificial grammar learning (AGL). The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato- thalamo-cortical (CSTC) neuroanatomy of the human language system. The challenge is to develop simulation models that take into account constraints both from neuranatomical connectivity, and from functional imaging data, and that can actually learn and perform the same kind of language and artificial syntax tasks. Thus different distributed neural networks will be trained and integrated in such a way that it understands the language as being understand by the human being.
