The document outlines the steps of natural language processing (NLP), emphasizing its importance in artificial intelligence and various applications like text and sentiment analysis. It details processes such as text preprocessing, text representation, model selection and evaluation, and finally tuning, optimization, and deployment of models. This serves as an introduction to key concepts and methodologies in the field of NLP for a student studying electrical engineering.

1. • Name : SOHOMGHOSH
• Roll No: 35001621017
• Registration No: 213500101610024
• Dept: ELECTRICAL ENGINEERING
• Subject: Artificial Intelligence
• Subject Code: OE-EE 701 A
• Semester: 7th
sem
• Session: 2021-2025
• College: Ramkrishna Mahato Government Engineering College, Purulia
• Year: 4th
year
Topic: The Steps of Natural Language
Processing (NLP)

2. Introduction to NLP
 Definition:
 Natural Language Processing (NLP) is a field of Artificial Intelligence
that focuses on the interaction between computers and human
languages.
 Importance:
 NLP is crucial for various applications like text analysis, sentiment
analysis, machine translation, chatbots, and more.

3. Step 1: Text Preprocessing
 Purpose: Clean and prepare raw text data.
 Key Processes:
 Tokenization: Splitting text into words/tokens.
 Lowercasing & Stop Word Removal: Ensuring uniformity and removing common
words.
 Stemming/Lemmatization: Reducing words to their base forms.

4. Step 2: Text Representation & Feature Engineering
 Text Representation:
 Bag of Words (BoW), TF-IDF: Basic word frequency-based methods.
 Word Embeddings: Advanced methods capturing semantic meaning
(e.g., Word2Vec, BERT).
 Feature Engineering:
 N-grams & POS Tagging: Capturing context and grammatical structure.
 Named Entity Recognition (NER): Identifying key entities like names,
dates.

5. Step 3: Model Selection, Training & Evaluation
 Model Selection:
 Algorithms: Choose from Naive Bayes, SVM, RNNs, Transformers, etc.
 Training:
 Feeding the processed data into the model for learning.
 Evaluation:
 Metrics: Accuracy, precision, recall, F1-score.
 Cross-Validation: Ensuring the model generalizes well.

6. Step 4: Tuning, Optimization & Deployment
 Tuning & Optimization:
 Hyperparameter Tuning: Adjusting learning rate, batch size, etc.
 Regularization: Techniques to prevent overfitting.
 Deployment:
 API Development & Monitoring: Integrating the model into production and
ensuring its ongoing performance.

7. Referance:
• https://www.geeksforgeeks.org/natural-language-processing-over
view/
• https://aws.amazon.com/what-is/nlp/#:~:text=Natural%20langua
ge%20processing%20(NLP)%20is,manipulate%2C%20and%20compreh
end%20human%20language
.
• https://www.ibm.com/topics/natural-language-processing

8. Thankyou