State-of the-Art: NLP Amazing Applications of Deep Learning
Gaddamidhi Sreevani1, V. A. Narayana2, Anuradha Boya3
1Gaddamidhi Sreevani, Department of CSE, CMR College of Engineering & Technology, Hyderabad (Telangana), India.
2Dr. V. A. Narayana, Department of CSE, CMR College of Engineering & Technology, Hyderabad (Telangana), India.
3Anuradha Boya, Department of CSE, CMR College of Engineering & Technology, Hyderabad (Telangana), India.
Manuscript received on 20 June 2019 | Revised Manuscript received on 11 July 2019 | Manuscript Published on 17 July 2019 | PP: 1222-1226 | Volume-8 Issue-1C2 May 2019 | Retrieval Number: A12180581C219/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Natural language processing is now not “solved“, but deep learning is required to get you to the brand new on many difficult problems in the field. Let’s seem to be examples to give you a snapshot of the results that deep learning is capable of car-rying out in NLP subject. over the previous few years, neural networks have re-emerged as effective system-getting to know models, yielding contemporary results in fields on the facet of text type, Speech popularity, Caption era, gadget Translation, file Summarization, query Answering, image reputation. all of the extra as of overdue, neural gadget models started out to be related furthermore to literary regular dialect symptoms, yet again with particularly encouraging effects. This instructional workout opinions neural system models from the element of view of normal dialect making prepared research, searching for to update not unusual dialect analysts with the neural strategies. This version communicate enter encoding for natural language duties, feed-ahead networks, convolution networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation. on this vein, we provide an define of the current deep learning Applications relevant to the Natural language processing. Moreover, we talk about various open research issues, which researchers may find helpful later on.
Keywords: Text Classification, Text translation, Speech Recognition, Image Caption Generation, Neural Networks, Deep Learning, Machine Learning, Speech-to-text, CNN,RNN.
Scope of the Article: Deep Learning