This repository is dedicated to Natural Language Processing (NLP) techniques and tools. NLP is a subfield of Artificial Intelligence that deals with the interaction between computers and human languages, including text and speech.
The main purpose of this repository is to provide resources and examples for those interested in NLP, including beginners and experts. The repository covers a wide range of NLP topics, including but not limited to:
- Text pre-processing and cleaning
- Text classification and clustering
- Named Entity Recognition (NER)
- Sentiment analysis
- Language modeling and generation
- Machine Translation
- Question Answering
- Speech Recognition
To get started with NLP, you will need a few things:
- Python 3.x
- Jupyter Notebook (optional but recommended)
- NLTK (Natural Language Toolkit) and/or SpaCy (Python libraries for NLP)
Once you have installed Python and Jupyter Notebook, you can install NLTK and/or SpaCy by running the following commands:
pip install nltk
pip install spacyNatural Language Understanding (NLU) is a subset of NLP that focuses on interpreting human language in a way that a machine can understand. NLU involves tasks such as identifying the intent of a sentence or extracting information from it.
NLU is an important component of many NLP applications, such as chatbots, virtual assistants, and sentiment analysis systems. In order to achieve accurate NLU, the machine must be able to understand the nuances of human language, including idioms, sarcasm, and ambiguity.
To achieve accurate NLU, several techniques are used, including:
- Named Entity Recognition (NER)
- Part-of-Speech (POS) tagging
- Dependency Parsing
- Sentiment Analysis
- Topic Modeling
- Word Embeddings
This repository includes examples and resources for NLU, including techniques and tools for achieving accurate understanding of human language.
Contributions to this repository are welcome. If you have any suggestions, bug fixes, or new examples to add, please feel free to submit a pull request.
This repository is licensed under the MIT License. See the LICENSE file for more information.