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This repo contains the code and datasets for paper "ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models".
The main features of stefanheng/proggen are: Information Extraction Frameworks, Named Entity Recognition, Synthetic Data Generation.
Projects with overlapping indexed features include: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… jinyuanli0012/pgim — Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge. allenai/beacon — This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper:… emma1066/self-improve-zero-shot-ner — This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot… arkhn/bio-nlp2023 — This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3… eulring/vaner — unikg.
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
This project is the codebase used for our weak supervision experiments using E3C dataset annotated with InstructGPT-3 and dictionary.
This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper: On-the-fly Definition Augmentation of LLMs for Biomedical NER
This is the github repository for the paper to be appeared at NAACL 2024 main conference: Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models.