https://github.com/amarnarayandwivedi/natural-language-processing-nlp-
Concepts of Natural language processing (NLP) in ML
https://github.com/amarnarayandwivedi/natural-language-processing-nlp-
Last synced: 7 months ago
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Concepts of Natural language processing (NLP) in ML
- Host: GitHub
- URL: https://github.com/amarnarayandwivedi/natural-language-processing-nlp-
- Owner: AmarNarayanDwivedi
- Created: 2024-11-20T17:13:02.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-01-08T07:13:33.000Z (9 months ago)
- Last Synced: 2025-01-20T09:13:39.295Z (9 months ago)
- Language: Jupyter Notebook
- Size: 4.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Natural-language-processing(NLP)
# Natural Language Processing (NLP) and Feature Engineering
## Overview
This repository contains a collection of Jupyter notebooks and resources for learning and implementing various NLP techniques and text preprocessing methods. The goal of this project is to cover essential NLP tasks such as tokenization, stemming, lemmatization, stopword removal, part-of-speech tagging, named entity recognition (NER), and vectorization techniques like TF-IDF and Word2Vec.
The project also demonstrates feature engineering methods used to preprocess and enhance the quality of data for machine learning models.
## Files in the Project
### 1. **Tokenization.ipynb**
- **Description**: This notebook introduces the concept of tokenization in Natural Language Processing (NLP). Tokenization is the process of breaking down a large text into smaller units such as words or sentences.
- **Key Topics**:
- Sentence tokenization
- Word tokenization
- Applications of tokenization in NLP tasks
### 2. **finalNLP.pdf**
- **Description**: This file contains a comprehensive overview of the NLP concepts and techniques covered in the project. It acts as a summary or final document to consolidate all the knowledge gained through the notebooks.
### 3. **Stemming and Its Types - Text Preprocessing.ipynb**
- **Description**: In this notebook, we explore stemming, which is the process of reducing words to their base or root form. This is an essential step in many NLP tasks for reducing the complexity of text.
- **Key Topics**:
- Introduction to stemming
- Types of stemming algorithms (e.g., Porter Stemmer, Lancaster Stemmer)
- Practical examples of stemming with Python and NLTK
### 4. **Lemmatization - Text Preprocessing.ipynb**
- **Description**: Lemmatization is another technique for reducing words to their base form, but unlike stemming, it considers the context and converts words into their meaningful lemma (dictionary form).
- **Key Topics**:
- Difference between stemming and lemmatization
- Lemmatization using NLTK and WordNet
- Practical examples
### 5. **Text Preprocessing - Stopwords with NLTK.ipynb**
- **Description**: This notebook focuses on removing stopwords, which are common words like "and", "the", "is", etc., that do not contribute meaningful information to the analysis.
- **Key Topics**:
- Identifying and removing stopwords from text
- Using NLTK library for stopword removal
- Impact of removing stopwords on text data
### 6. **Parts of Speech Tagging.ipynb**
- **Description**: This notebook covers parts of speech (POS) tagging, a technique used to assign word types such as noun, verb, adjective, etc., to each word in a sentence.
- **Key Topics**:
- Introduction to POS tagging
- Tagging words with POS using NLTK
- Applications of POS tagging in NLP tasks
### 7. **Named Entity Recognition.ipynb**
- **Description**: Named Entity Recognition (NER) is used to identify and classify named entities in text, such as names of people, organizations, locations, dates, etc.
- **Key Topics**:
- Understanding named entities
- Using NLTK and spaCy for NER
- Practical NER examples with text
### 8. **Bag of Words Practical's.ipynb**
- **Description**: This notebook introduces the Bag of Words (BoW) model, which is a common technique used to represent text data in machine learning tasks.
- **Key Topics**:
- Creating a Bag of Words model using Scikit-learn
- Vectorizing text data for classification
- Applications of BoW in machine learning tasks
### 9. **TF-IDF Practical.ipynb**
- **Description**: This notebook demonstrates the TF-IDF (Term Frequency-Inverse Document Frequency) method for converting text into a vector form based on the importance of terms in the text.
- **Key Topics**:
- Understanding TF-IDF
- TF-IDF implementation using Scikit-learn
- Comparing TF-IDF with Bag of Words
### 10. **Word2Vec Practical Implementation.ipynb**
- **Description**: This notebook covers Word2Vec, a powerful word embedding technique that represents words as vectors in a continuous vector space. It is widely used in modern NLP for capturing semantic meanings of words.
- **Key Topics**:
- Introduction to Word2Vec
- Implementing Word2Vec using Gensim
- Applications of Word2Vec in NLP tasks
---
## Feature Engineering
Feature engineering plays a crucial role in preparing text data for machine learning models. In this project, we learned various techniques for preprocessing and transforming raw text data into meaningful features that can be used for predictive tasks. Some of the key techniques demonstrated include:
- **Tokenization**: Converting raw text into smaller, structured units such as words or sentences.
- **Stemming and Lemmatization**: Reducing words to their base or root forms to reduce complexity.
- **Stopword Removal**: Filtering out common words that do not add value to analysis.
- **POS Tagging**: Labeling words with their respective parts of speech.
- **Named Entity Recognition (NER)**: Identifying entities like names, dates, locations, etc., in the text.
- **Vectorization**: Converting text data into numerical form using techniques like Bag of Words, TF-IDF, and Word2Vec.
These techniques form the foundation of feature engineering in NLP, making raw text data more useful and informative for machine learning algorithms.
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