Projects sharing features with Information Extraction

Entity-Relation-Extraction is a machine learning framework designed to identify entities and their logical connections within unstructured text. It functions as a pipeline that transforms raw documents into structured knowledge graphs by utilizing deep learning models and transformer architectures. The project distinguishes itself through a schema-driven approach, which maps extracted information to predefined relational templates to ensure output consistency. It employs a multi-stage process that combines sequence-labeling token classification with contextual encoding to delineate entity bou

Comparison of Chinese Named Entity Recognition Models between NeuroNER and BertNER

Entity Linker solution

This repository contains the source code and dataset for the paper: PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction. Hengyi Zheng, Rui Wen, Xi Chen et al. ACL 2021.

Knowledge triples extraction and knowledge base construction based on dependency syntax for open domain text.

:deciduous_tree:从中文文本中自动提取关键词和摘要

A concept and obvious expression pattern collection of Chinese compound event extraction which then be evolved into ComplexEventGraph，本项目提出了中文复合事件的概念与显式模式，包括条件事件、因果事件、顺承事件、反转事件等事件抽取，并形成事理图谱。

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

This is a named entity recognizer based on BERT Model(pytorch-pretrained-BERT) and CRF.

This project is a named entity recognition framework and TensorFlow-based natural language processing model. It provides a pipeline for adapting pre-trained language models to specific entity recognition and text classification tasks. The system implements a sequence labeling architecture that combines transformer-based embeddings with bidirectional sequence modeling and conditional random field decoding. It includes tools for fine-tuning model weights and training the network to identify and categorize entities within unstructured text. The framework also includes a client-server architectu

Information Extraction in Python

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text

MITIE: library and tools for information extraction

A simple resume parser used for extracting information from resumes

更新了ietorchycy.py 跟 tokenfunc.py 利用纯pytorch实现，后期封装成服务，使其能更好的在线上运行~ —————————————————————— 运行ietorch4.py 详情请见知乎 https://zhuanlan.zhihu.com/p/138858558

使用预训练语言模型BERT做中文NER

中文语句中的时间语义识别。即通过分析中文语句，识别出话语中提到的时间。

Neural Relation Extraction implemented in PyTorch

Source code of the ACL 2022 paper "Packed Levitated Marker for Entity and Relation Extraction".

中文地址分词（地址元素识别与抽取），通过序列标注进行NER

构建医疗实体识别的模型，包含词典和语料标注，基于python构建

A Toolkit for Industrial Topic Modeling

A neural network model for Chinese named entity recognition

Python Keyphrase Extraction module