keycare 0.0.9

KeyCARE is a Python library designed for the unsupervised keyword extraction from biomedical documents with the use of different algorithms, the classification of the keywords according to their semantic nature, and the extraction of is a relations among those keywords and with other terminologies.

KeyCARE

A python library for biomedical keyword extraction, term categorization, and semantic relation

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Table of Contents

1. About the Project
2. Getting Started
   2.1. Installation
   2.2. Usage
3. Contributing
4. License
5. References

1. About The Project

Back to ToC KeyBERT provides a common interface for extracting, categorizing and associating terms extracted from a text:

1. Keywords extraction: KeyCARE implements several unsupervised term extraction techniques such as YAKE, RAKE, TextRank or KeyBERT to automatically extract key terms from a text.
2. Term categorization: KeyCARE allows the application of term clustering techniques to group similar terms, as well as the training and application of supervised techniques to classify keywords into predefined categories, including [SetFit].
3. Semantic relation classification: Beyond the identification and categorization of terms, the library supports the use of neural classification models to extract the semantic relation between two terms by means of EXACT, BROAD, NARROW and NO_relation relationships, which allows interconnecting the extracted terms and can be used for terminological enrichment, among other tasks.

2. Getting Started

Back to ToC

2.1. Installation

Installation can be done using pypi:

   pip install keycare

2.2. Usage

The library is built on 3 main processes:

Term Extraction

...

Term Categorization

Relation Extraction

For further information on the functioning of the library refer to the tutorials in the nbs folder.

3. Contributing

Back to ToC

This library has been developed with Python 3.8.2

Any contributions you make are greatly appreciated. For contributing:

1. Fork/Clone the Project in your system

   git clone https://github.com/nlp4bia-bsc/keycare.git
   

2. Create a new virtual environment

   python3 -m venv .env_keycare
   

3. Activate the new environment

   source .env_keycare/bin/activate
   

4. Install the requirements

   pip install -r requirements.txt
   

5. Create your Feature Branch (git checkout -b feature/AmazingFeature)

6. Update requirements file (pip freeze > requirements.txt)

7. Commit your Changes (git commit -m 'Add some AmazingFeature')

8. Push to the Branch (git push origin feature/AmazingFeature)

9. Open a Pull Request from github.

Follow this tutorial to create a branch.

4. License

Back to ToC

Apache License, Version 2.0

5. References

Back to ToC

Please cite if you use the library in scientific works:

PAPER TO BE PUBLISHED