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Code, data, and results described in the paper "Mining experimental data from materials science literature with large language models: an evaluation study", https://www.tandfonline.com/doi/full/10.1080/27660400.2024.2356506
The main features of lfoppiano/matsci-lumen are: In Context Learning, Information Extraction Frameworks, Named Entity Recognition, Zero-Shot Prompting.
Projects with overlapping indexed features include: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… 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… allenai/beacon — This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper:… zhaoyuesun/phee-with-chatgpt — Code for "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study" (EACL 2024). tangxuemei1995/chisiec — CHisIEC: An Information Extraction Corpus for Ancient Chinese History. toneli/rt-retrieving-and-thinking — This is the source code of the model RT (Retrieving and Thinking). For the full project, please check the file…
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 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.
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
CHisIEC: An Information Extraction Corpus for Ancient Chinese History