https://github.com/nicolay-r/AREkit

Document level Attitude and Relation Extraction toolkit (AREkit) for sampling and processing large text collections with ML and for ML
https://github.com/nicolay-r/AREkit

bert datasets frames language-models neural-networks nlp pandas pandas-dataframe prompt prompting relation-extraction sentiment-analysis tensorflow

Last synced: 13 days ago
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Document level Attitude and Relation Extraction toolkit (AREkit) for sampling and processing large text collections with ML and for ML

• Host: GitHub
• URL: https://github.com/nicolay-r/AREkit
• Owner: nicolay-r
• License: mit
• Created: 2019-12-03T20:20:46.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-09-29T16:31:34.000Z (about 2 months ago)
• Last Synced: 2024-09-29T16:34:47.396Z (about 2 months ago)
• Topics: bert, datasets, frames, language-models, neural-networks, nlp, pandas, pandas-dataframe, prompt, prompting, relation-extraction, sentiment-analysis, tensorflow
• Language: Python
• Homepage: https://nicolay-r.github.io/arekit-page/
• Size: 22.4 MB
• Stars: 56
• Watchers: 6
• Forks: 3
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AREkit 0.25.0

![](https://img.shields.io/badge/Python-3.9+-brightgreen.svg)



    



**AREkit** (Attitude and Relation Extraction Toolkit) --

is a python toolkit, devoted to document level Attitude and Relation Extraction between text objects from mass-media news. 

## Description

This toolkit aims at memory-effective data processing in Relation Extraction (RE) related tasks.



    



> Figure: AREkit pipelines design. More on 

> **[ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction](https://www.ecir2024.org/accepted-paper/)** paper

In particular, this framework serves the following features: 

* ➿ [pipelines](https://github.com/nicolay-r/AREkit/wiki/Pipelines:-Text-Opinion-Annotation) and iterators for handling large-scale collections serialization without out-of-memory issues.

* 🔗 EL (entity-linking) API support for objects, 

* ➰ avoidance of cyclic connections,

* :straight_ruler: distance consideration between relation participants (in `terms` or `sentences`),

* 📑 relations annotations and filtering rules,

* *️⃣ entities formatting or masking, and more.

The core functionality includes: 

* API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support 

for sentence level relations preparation (dubbed as contexts);

* API for contexts extraction;

* Relations transferring from sentence-level onto document-level, and more.

## Installation 

```bash

pip install git+https://github.com/nicolay-r/[email protected]

```

## Usage

Please follow the **[tutorial section on project Wiki](https://github.com/nicolay-r/AREkit/wiki/Tutorials)** for mode details.

## How to cite

A great research is also accompanied by the faithful reference. 

if you use or extend our work, please cite as follows:

```bibtex

@inproceedings{rusnachenko2024arelight,

  title={ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction},

  author={Rusnachenko, Nicolay and Liang, Huizhi and Kolomeets, Maxim and Shi, Lei},

  booktitle={European Conference on Information Retrieval},

  year={2024},

  organization={Springer}

}

```