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Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024).
DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured knowledge graphs. It provides a pipeline for identifying and classifying named entities, semantic relations, and events, converting raw datasets into structured triples. The project utilizes large language models as tool callers through a standardized context protocol to drive automated data extraction processes. It supports schema-driven extraction across multiple domains and bilingual text, employing joint entity and relation extraction to identify components in a single structured
A list of NLP resources focused on event extraction task
This repository contains the code and resources accompanying the paper titled "Advancing Entity Recognition in Biomedicine via Instruction Tuning of Large Language Models." The paper explores the use of instruction-based learning paradigm to transform biomedical Named Entity Recognition (NER)…
Source code and data for ACL 2023 main conference paper DICE: Data-Efficient Clinical Event Extraction with Generative Models.
The main features of derekmma/dice are: Event Extraction, Information Extraction Frameworks.
Projects with overlapping indexed features include: zhaoyuesun/phee-with-chatgpt — Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024). zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… baptisteblouin/eventextractionpapers — A list of NLP resources focused on event extraction task. cocacola-lab/collabkg — CollabKG is an open-source IE annotation toolkit that unifies NER, RE, and EE tasks, integrates KG and EKG, and… d-gurgurov/injecting-commonsense-knowledge-into-llms — Project Overview:. bids-xu-lab/bioner-llama — This repository contains the code and resources accompanying the paper titled "Advancing Entity Recognition in…