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.

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 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.

Self Improve Zero Shot NER के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो Self Improve Zero Shot NER के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।

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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
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lfoppiano/matsci-lumen
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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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अक्सर पूछे जाने वाले प्रश्न

emma1066/self-improve-zero-shot-ner क्या करता है?

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.

emma1066/self-improve-zero-shot-ner की मुख्य विशेषताएं क्या हैं?

emma1066/self-improve-zero-shot-ner की मुख्य विशेषताएं हैं: In Context Learning, Information Extraction Frameworks, Named Entity Recognition, Zero-Shot Prompting।

emma1066/self-improve-zero-shot-ner के कुछ ओपन-सोर्स विकल्प क्या हैं?

emma1066/self-improve-zero-shot-ner के ओपन-सोर्स विकल्पों में शामिल हैं: zjunlp/deepke — DeepKE is a knowledge extraction toolkit and framework designed to transform unstructured text into structured… lfoppiano/matsci-lumen — Code, data, and results described in the paper "Mining experimental data from materials science literature with large… 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). 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… tangxuemei1995/chisiec — CHisIEC: An Information Extraction Corpus for Ancient Chinese History.

Emma1066Self-Improve-Zero-Shot-NER