¿Cuáles son las características principales de emma1066/self-improve-zero-shot-ner?

Las características principales de emma1066/self-improve-zero-shot-ner son: In Context Learning, Information Extraction Frameworks, Named Entity Recognition, Zero-Shot Prompting.

Self Improve Zero Shot NER by Emma1066

Name: emma1066/self-improve-zero-shot-ner
Author: Emma1066

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.

Features

In Context Learning - Self-improving mechanism for zero-shot entity recognition.
Information Extraction Frameworks - Self-improvement loop for zero-shot entity recognition tasks.
Named Entity Recognition - Self-improving framework for zero-shot entity recognition.
Zero-Shot Prompting - Self-improving mechanism for zero-shot entity recognition.

Historial de estrellas

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.

Features

In Context Learning - Self-improving mechanism for zero-shot entity recognition.
Information Extraction Frameworks - Self-improvement loop for zero-shot entity recognition tasks.
Named Entity Recognition - Self-improving framework for zero-shot entity recognition.
Zero-Shot Prompting - Self-improving mechanism for zero-shot entity recognition.

zjunlp/DeepKE

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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

lfoppiano/MatSci-LumEn

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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

allenai/beacon

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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

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