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.
Project description
KeyCARE
A python library for biomedical keyword extraction, term categorization, and semantic relation
Table of Contents
1. About The Project
Back to ToC KeyBERT provides a common interface for extracting, categorizing and associating terms extracted from a text:
- Keywords extraction: KeyCARE implements several unsupervised term extraction techniques such as YAKE, RAKE, TextRank or KeyBERT to automatically extract key terms from a text.
- 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].
- 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
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
This library has been developed with Python 3.8.2
Any contributions you make are greatly appreciated. For contributing:
-
Fork/Clone the Project in your system
git clone https://github.com/nlp4bia-bsc/keycare.git
-
Create a new virtual environment
python3 -m venv .env_keycare
-
Activate the new environment
source .env_keycare/bin/activate
-
Install the requirements
pip install -r requirements.txt
-
Create your Feature Branch (
git checkout -b feature/AmazingFeature
) -
Update requirements file (
pip freeze > requirements.txt
) -
Commit your Changes (
git commit -m 'Add some AmazingFeature'
) -
Push to the Branch (
git push origin feature/AmazingFeature
) -
Open a Pull Request from github.
Follow this tutorial to create a branch.
4. License
5. References
Please cite if you use the library in scientific works:
PAPER TO BE PUBLISHED
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