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The model identifies chemical components and genes named entities and extracts the relations of the chemical-gene pair jointly. It utilizes the BioBERT model in the named entity recognition and the graphs neural networks for the RE subtasks.

Python 100.00%
biobert graphneuralnetwork named-entity-recognition natural-language-processing relation-extraction pytorch

chemical-gene-chemical-gene-relation-extraction-with-gnn's Introduction

Hi 👋

I am Mina Kambar, a Ph.D. candidate and a research assistant in the department of Computer Science at UNLV. Here are what I like to learn more about:

  • I have a three-year background in software development, where I contributed to Android Applications, website design, and Java backend server teams. My passion for software-related work remains unwavering. 🌱

  • Over the past four years, I've delved deep into the world of Machine Learning and Deep Learning, exploring their applications across diverse domains. 😇

  • Natural Language Processing holds a special place in my heart. The concept of enabling computers to comprehend human language fascinates me. I've honed my skills in text processing, particularly in the context of medical texts. Currently, I am a research graduate assistant at UNLV, dedicated to advancing the field by working on Large Language Models (LLMs) for medical corpora.

    Where to find me:


GIF


languages and tools

chemical-gene-chemical-gene-relation-extraction-with-gnn's People

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chemical-gene-chemical-gene-relation-extraction-with-gnn's Issues

Architecture

Hello,
I'm confused it seems that your gnn layers are not used anywhere in GNN class? If they actually are used somewhere can you point out where ? Thank you

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