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CHisIEC: An Information Extraction Corpus for Ancient Chinese History
The main features of tangxuemei1995/chisiec are: In Context Learning, Information Extraction Frameworks, Named Entity Recognition, Relation Extraction.
Projects with overlapping indexed features include: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… mayubo2333/llm-ie — This is the implementation of filter-then-rerank pipeline in Large Language Model Is Not a Good Few-shot Information… allenai/beacon — This repository contains code to run the inference and evaluation of NER as described in our NAACL 2024 paper:… lbnlp/nerre-llama — If you are just looking to download the LoRA weights directly, use this url:… 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… lfoppiano/matsci-lumen — Code, data, and results described in the paper "Mining experimental data from materials science literature with large…
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
If you are just looking to download the LoRA weights directly, use this url: https://figshare.com/ndownloader/files/43044994 and view the data entry on Figshare.
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.