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@@ -11,22 +11,50 @@ is a python toolkit, devoted to document level Attitude and Relation Extraction | |
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## Description | ||
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This toolkit aims to solve data preparation problems in Relation Extraction related taks, considiering such factors as: | ||
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This toolkit aims at memory-effective data processing in Relation Extraction (RE) related tasks. | ||
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<p align="center"> | ||
<img src="docs/arekit-pipeline-concept.png"/> | ||
</p> | ||
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> 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. | ||
* ➿ [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. | ||
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The core functionality includes | ||
(1) API for document presentation with EL (Entity Linking, i.e. Object Synonymy) support | ||
for sentence level relations preparation (dubbed as contexts) | ||
(2) API for contexts extraction | ||
(3) relations transferring from sentence-level onto document-level, 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. | ||
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## Installation | ||
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```bash | ||
pip install git+https://github.com/nicolay-r/[email protected] | ||
``` | ||
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## Usage | ||
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Please follow the **[tutorial section on project Wiki](https://github.com/nicolay-r/AREkit/wiki/Tutorials)** for mode details. | ||
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## How to cite | ||
A great research is also accompanied by the faithful reference. | ||
if you use or extend our work, please cite as follows: | ||
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```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} | ||
} | ||
``` |
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