项目介绍

gkiril/oie-resources 是一个托管在 GitHub 上的开源项目,目前获得 508 个 Star、54 次 Fork。

仓库全名
gkiril/oie-resources
Star 数
508
Fork 数
54

README 摘要

Open Information Extraction (OIE) Resources A curated list of Open Information Extraction (OIE) resources: research papers, code, data, applications, etc. The list is not limited to Open Information Extraction systems exclusively. It also includes work highly related to OIE, such as taxonomizing open relations and using OIE in downstream applications. Table of contents Introduction to OIE Papers sorted in…

完整内容见 GitHub 原始 README 或本页 。

常见问题

如何获取 oie-resources 的源码?

可以直接克隆仓库:git clone https://github.com/gkiril/oie-resources.git,默认分支为 master;安装与运行步骤见仓库 README。

oie-resources 最近还在维护吗?

仓库最近一次更新为 2022年10月25日,当前没有未关闭的 Issue;Star 数为 508,可作为社区活跃度的参考。

关于

GitHub 原始描述(英文)

A curated list of Open Information Extraction (OIE) resources: papers, code, data, etc.

描述取自 GitHub 官方 API,保持仓库作者书写的原文。

README

Open Information Extraction (OIE) Resources

A curated list of Open Information Extraction (OIE) resources: research papers, code, data, applications, etc. The list is not limited to Open Information Extraction systems exclusively. It also includes work highly related to OIE, such as taxonomizing open relations and using OIE in downstream applications.

Table of contents

Introduction to OIE

Open Information Extraction (OIE) systems aim to extract unseen relations and their arguments from unstructured text in unsupervised manner. In its simplest form, given a natural language sentence, they extract information in the form of a triple, consisted of subject (S), relation ® and object (O).

Suppose we have the following input sentence:

AMD, which is based in U.S., is a technology company.

An OIE system aims to make the following extractions:

("AMD"; "is based in"; "U.S.")
("AMD"; "is"; "technology company")

Papers sorted in chronological order

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

Papers grouped by category

Surveys

Evaluation

OIE for downstream applications

OIE’s output has been shown to be a useful input for many downstream tasks. In this section, several downstream tasks that benefited from OIE output are listed.

Question Answering
Slot Filling
Event Extraction
Text Summarization
Knowledge Base Population
Knowledge Base Construction
Entity Linking
Relation Linking
Relation Extraction
Relating Entities
Story Comprehension
Text Generation
Video Grounding

OIE in Different Languages

Most of the OIE systems are focused on extractions made from text written on English. However, some OIE systems either are focused on a language other than English, or are multilingual. In this section, OIE systems on languages other than English or multilingual OIE systems are listed.

Multilingual OIE Systems
OIE Systems for German Language
OIE Systems for Portugese Language
OIE Systems for Spanish Language
OIE Systems for Chinese Language

README 内容过长,已截取前 64 KB。完整内容请前往 GitHub 仓库查看。

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