Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging
Introduction
This is the code for Persian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging, published in the Findings of the Association for Computational Linguistics: EMNLP 2020.
The codes are written in Python 3.6.9 using the PyTorch framework in Jupyter Notebook format. The file names are similar to the names of the models introduced in the paper.
Requirements
You can find the required libraries in the requirements.txt file.
Citation
@inproceedings{doostmohammadi-etal-2020-persian,
title = "{P}ersian Ezafe Recognition Using Transformers and Its Role in Part-Of-Speech Tagging",
author = "Doostmohammadi, Ehsan and
Nassajian, Minoo and
Rahimi, Adel",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.86",
doi = "10.18653/v1/2020.findings-emnlp.86",
pages = "961--971",
}
پروژههای مشابه
code for our paper "Understanding by Understanding Not: Modeling Negation in Language Models"
In this repository, we provide a neural model based on BERT and BiLSTM neural networks, which can recognize the Kasreh Ezafeh (Genitive Conjunction /e/) in a Persian sentence or text.
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