Building an RNN model to predict the next word that will appear in a sentence is a problem in natural language processing

Introduce

Simulation image of RNN neural network

Visualization after word embedding

Environment setup

1. Install Python libraries: numpy,tensorflow,scikit-learn, regex .
2. Dataset:

• Text data is used from "Speech of the Rector of Ho Chi Minh City University of Technology and Education at the ceremony celebrating 50 years of Vietnam-Germany relations"

• Link: https://hcmute.edu.vn/?ArticleId=aead837e-8612-4ce7-80b0-6ae40dca6a53.

• The dataset includes 518 words

Steps to build a model in the project

1. Preprocessing:

       - Perform data preprocessing steps, including sentence extraction, meaningful word matching, white space removal, punctuation removal, word dictionary   
        generation, and input sequence generation using the n-gram method.
   

2. Word Embedding:

       -I use a word embedding layer to reduce the representation size of words. To improve computing and learning abilities.
   
       -I represent words in a multidimensional vector space to capture semantic relationships.
   

3. Recurrent Neural Network (RNN):

       - I built a deep learning architecture, including embedding layers and SimpleRNN to train the model.
   
       - I used TensorFlow and Keras libraries to develop and evaluate the model
   

4. Performance evaluation

       - Used metrics such as accuracy, precision, recall, and F1 score to evaluate the performance of RNN models on the test set.
   

Libraries and Technology

• Programming language: Python
• Main libraries: numpy, scikit-learn, tensorFlow, regex
• Model: RNN

Oriented development

• I will use a larger data set to build the model.
• I will use other models such as LSTM and GRU to overcome the limitation of the RNN model that the derivative in the back propagation process is exploded or vanishing.
• I will use the contextual word separation method for better performance

Conclusion

• This project provides an overview of different NLP techniques and how to implement them for text processing and analysis. The models were trained and evaluated on real-world text data to ensure their effectiveness in handling natural language tasks.