Projects sharing features with DILUIE

This repository contains a PyTorch implementation of the autoregressive information extraction system GenIE proposed in the paper GenIE: Generative Information Extraction. We extend these ideas in our follow-up work on SynthIE, visit this link for details. ``…

Code for our ACL-2022 paper Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction.

UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, Hoifung Poon (*Equal Contribution)

This is the code for ACL2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis

Rethinking Negative Instances for Generative Named Entity Recognition

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

Code for the paper "Structured Prediction as Translation between Augmented Natural Languages" (ICLR 2021) and fine-tuned multi-task model.

This is the official repository for IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

This is the official repository for "CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors" (ACL 2023).

This repository contains the code and resources accompanying the paper titled "Advancing Entity Recognition in Biomedicine via Instruction Tuning of Large Language Models." The paper explores the use of instruction-based learning paradigm to transform biomedical Named Entity Recognition (NER)…