This repository contains Natural Language Processing programs in the Python programming language.

1. Definition: Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.

2. Goal: The primary goal of NLP is to bridge the gap between human communication and computer understanding. It involves the development of algorithms and models that allow computers to interact with and analyze textual or spoken language.

3. Key Tasks: NLP encompasses a variety of tasks, including text tokenization (breaking text into words or phrases), part-of-speech tagging (assigning grammatical categories to words), sentiment analysis (determining emotional tone), and machine translation (translating text from one language to another).

4. Challenges: NLP faces challenges due to the complexity and ambiguity of human language, including variations in syntax, semantics, and context. Ambiguities in language make tasks such as natural language understanding and generation inherently challenging.

5. Applications: NLP has a wide range of applications, from chatbots and virtual assistants to language translation services and sentiment analysis for social media. It plays a crucial role in extracting meaningful insights from vast amounts of unstructured textual data, contributing to advancements in various industries.

1. Convert the text into tokens.

2. Find the word frequency.

3. Demonstrate a bigram language model.

4. Demonstrate a trigram language model.

5. Generate a regular expression for a given text.

6. Perform Lemmatization.

7. Perform Stemming.

8. Identify parts-of-speech using Penn Treebank tag set.

9. Implement HMM for POS tagging.

10. Build a Chunker.

Drop a 🌟 if you find this repository useful.

If you have any doubts or suggestions, feel free to reach me.

📫 How to reach me: