Next Words Prediction Using Recurrent Neural Networks

Project Title

Next Words Prediction Using Recurrent Neural Networks

Project Area of Specialization Artificial IntelligenceProject Summary

Natural Language Processing (NLP) is a significant part of artificial Intelligence, which incorporates AI, which contributes to finding productive approaches to speak with people and gain from the associations with them. One such commitment is to give portable clients anticipated ”next words,” as they type along within applications, with an end goal to assist message conveyance by having the client select a proposed word as opposed to composing it. As LSTM is Long short time memory it will understand the past text and predict the words which may be helpful for the user to frame sentences and this technique uses a letter to letter prediction means it predicts a character to create a word. As writing an essay and framing a big paragraph are time-consuming it will help end-users to frame important parts of the paragraph and help users to focus on the topic instead of wasting time on what to type next. We expect to create or mimic auto-complete features using LSTM. Most of the software uses different methods like NLP and normal neural networks to do this task we will be experimenting with this problem using LSTM by using the Default Nietzsche text file also known as our training data to train a model. Next Word Prediction is also called Language Modeling that is the task of predicting what word comes next. It is one of the fundamental tasks of NLP and has many applications.

Project Objectives

1.Trying to create model using Nietzsche default text file which will predict users sentence after the users typed 40 letters, the model will understand 40 letters and predict upcoming letter/words using LSTM neural network which will be implemented using Tensorflow.

2. To assist message conveyance by having the client select a proposed word as opposed to composing it.

3.To help users to focus on the topic instead of wasting time on what to type next.

4.To predict 10 or more then 10 word as fast as possible utilizing minimum time

Project Implementation Method

This project is made to create a flexible model that can help users to detect next word while understanding user vocable in a fast and effective manner so user need to provide 40 letters then it passes this letter to LSTM NN and predicts N number of letters

 • providing input data up to 40 letters later this sentence will pass through LSTM Neural Network

• Letter LSTM understand and learn every letter, letter by letter and create a score for the next letter.

• This score then again will pass through the same LSTM and later it will predict a word letter by letter.

 • Below is our neural network architecture plus our implementation methodology using the Tensorflow library.

 – Letter to bits, As computers, don’t understand words so converting words to bits or array of bits using NumPy software.

 – Now creating a 3D array of all words it’s like one-hot encoding for all letter and unique characters (200285, 40, 57) this was our training data

– Later passing this X features to our model with input Neural node 40 and hidden node 128 then this will have an output layer with node equal to the input node.

Benefits of the Project

This product has more scope on social media for syntax analysis and semantic analysis in natural language processing in Artificial intelligence.

As writing an essay and framing a big paragraph are time-consuming it will help end-users to frame important parts of the paragraph and help users to focus on the topic instead of wasting time on what to type next.

Technical Details of Final Deliverable

It is the undertaking of predicting what word comes straightaway. It is one of the major assignments of NLP and has numerous applications. Attempting to make model utilizing nietzsche default text record which will foresee clients sentence after the clients composed 40 letters, the model will comprehend 40 letters and anticipate impending top 10 words utilizing RNN neural organization which will be executed utilizing Tensorflow. Our Aim of creating this model to predict 10 or more then 10 word as fast as possible utilizing minimum time. As RNN is Long short time memory it will understand past text and predict the words which may be helpful for the user to frame sentences and this technique uses letter to letter prediction means it predict a letter after letter to create a word.

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Education Core Technology Artificial Intelligence(AI)Other Technologies Cloud Infrastructure, OthersSustainable Development Goals Good Health and Well-Being for People, Quality EducationRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	79500
Stationary and thesis	Miscellaneous	2	5000	10000
All in one HP Printer	Equipment	1	38000	38000
Data collection	Equipment	1	10000	10000
Veiwsonic LCD for testing double text	Equipment	1	12000	12000
2TB hard disk for data storage	Equipment	1	9500	9500