DARNN: Discourse Analysis for Natural languages using RNN and LSTM - Section Original Research
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Abstract
Natural language processing (NLP) is a computer science arena concerned with computer connections and human
(natural) language connections. One of the significant subfunctions of any application to the NLP is discourse
processing. Discourse processing is intended to uncover information from certain information that is understood
naturally (NLU). In fulfilling this task, we used special neural networks (RNN with LSTM). Recurrent neural
networks are the prerequisites for (RNN). However, RNN does have some of the disadvantages of LSTM (Long
Short-Term Memory). Kaggle and Keras are collected with data. The designed system proposed provides LSTM
with 72% accuracy. With more text generation deep learning algorithms, the same approach can also be used.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
tayal, madhuri, & Tayal, A. . (2021). DARNN: Discourse Analysis for Natural languages using RNN and LSTM: -. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.471
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