Understanding and Responding Text Data with Categories Using Bi-Directional LSTM and NLP |
Author(s): |
| Akshaya K , S.A Engineering College; Rakshitha T, S.A Engineering College; Swathi J, S.A Engineering College; Julia Faith S, S.A Engineering College |
Keywords: |
| Natural Language Processing (NLP), Long Short Term Memory (LSTM), Data Sparseness, Bi-directional LSTM, Latitudinal Explosion |
Abstract |
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The old text classification methods are based on machine learning. It requires a large amount of artificially labelled training data as well as human participation. However, it is common that ignoring the contextual information and the word order information in such a way, and often happen some problems such as data sparseness and latitudinal explosion. With the development of deep learning, many researchers have also been using deep learning in text classification. In our project we investigates the application issue of NLP in text classification by using the Bi-Directional LSTM method. The impact of Bi-Directional LSTM with native LSTM model will also be experimented in our project. |
Other Details |
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Paper ID: IJSRDV9I20342 Published in: Volume : 9, Issue : 2 Publication Date: 01/05/2021 Page(s): 460-461 |
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