The document is a report on the steps of Natural Language Processing (NLP) authored by Sohom Ghosh, an electrical engineering student. It outlines definitions, importance, and the four main steps involved in NLP: text preprocessing, text representation & feature engineering, model selection, training & evaluation, and tuning, optimization & deployment. The report includes references for further reading on NLP applications and methods.

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)
• Name : SOHOMGHOSH
• Roll No: 35001621017
• Registration No: 213500101610024
• Dept: ELECTRICAL ENGINEERING
• Subject: Artificial Intelligence
• Subject Code: OE-EE 701 A
• Semester: 7th
sem
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