WORD BASED LANGUAGE MODEL USING LONG SHORT TERM MEMORY FOR DISABILITIES
DOI:
https://doi.org/10.61841/t73f5m36Keywords:
Autism Spectrum Disorder, Long Short-Term Memory, , Language Modeling, Natural Language ProcessingAbstract
Identification of Autism Spectrum Disorder (ASD) among children plays a vital role to develop their social interaction through early intervention programme. Our proposed model categorizes the children who affected with ASD from typically developed children based on their linguistic knowledge. Existing methods are lagging with short-term memory and early saturation of learning rate. Our proposed model solves the demerits of existing approaches and has the advantages of classifying the children with ASD accurately with high precision and recall as well as a new model of LSTM based deep learning neural network has used. Word prediction is useful for disabled persons and IQ of Autism children will be tested based on latent semantic analysis. The experimental results showed in this paper promisingly.
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References
1. Piotr Klosowki, “Deep Learning for Natural Language Processing and Language Modeling”, IEEE, 2018.
2. Nimesh Sinha, “Understanding LSTM and its quick implementation in Keras for sentiment analysis”, Towardsdatascience, Feb. 20, 2018.
3. Shashi Pal Singh, Ajai Kumar, Daya Chand Mandal, Yasha Jadwani, “Word and Phrase Prediction Tool for English and Hindi Language”, ICEEOT, 2016.
4. Nebojsa D. Grujic, Vladimir M. Milovanovic, “Natural Language Processing for Associative Word Predictions”, IEEE, 2019.
5. M.P.Bhuyan, S.K.Sharma, “Effects of Prediction Length on Accuracy in Automatic Assamese Word Prediction”, IEEE, 2019.
6. Deepa Nagalavi, M.Hanumanthappa, “N-gram Word Prediction Language Models to Identify the Sequence of Article Blocks in E – Newspapers”, IEEE, 2016.
7. Shalini Ghosh et al., “Contextual LSTM (CLSTM) models for Large Scale NLP tasks”, ACM, 2016.
8. Sanchari Sen, Anand Raghunathan, “Approximate Computing for Long Short Term Memory(LSTM) Neural Networks”, IEEE, 2018.
9. Aravind Kannan, Dr.V.Thiruvalluvan, “Application of Natural Language Processing Techniques to Augmentative Communication Systems”.
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