人工智能-项目实践-实体抽取-基于Python的实体关系抽取

# open-entity-relation-extraction Knowledge triples extraction (entities and relations extraction) and knowledge base construction based on dependency syntax for open domain text. 基于依存句法分析，实现面向开放域文本的知识三元组抽取（实体和关系抽取）及知识库构建。 Welcome to watch, star or fork. ## Example > "中国国家主席习近平访问韩国，并在首尔大学发表演讲" We can extract knowledge triples from the sentence as follows: - (中国, 国家主席, 习近平) - (习近平, 访问, 韩国) - (习近平, 发表演讲, 首尔大学) ## Project Structure ``` knowledge_extraction/ |-- code/ # code directory | |-- bean/ | |-- core/ | |-- demo/ # procedure entry | |-- tool/ |-- data/ # data directory | |-- input_text.txt # input text file | |-- knowledge_triple.json # output knowledge triples file |-- model/ # ltp models, can be downloaded from http://ltp.ai/download.html, select ltp_data_v3.4.0.zip |-- resource # dictionaries dirctory |-- requirements.txt # dependent python libraries |-- README.md # project description ``` ## Requirements This repo was tested on Python 3.5+. The requirements are: - jieba>=0.39 - pyltp>=0.2.1 ## Quickstart ```shell cd ./code/demo/ python extract_demo.py ``` ## Seven DSNF paradigms  ## References If you use the code, please kindly cite the following paper: Jia S, Li M, Xiang Y. Chinese Open Relation Extraction and Knowledge Base Establishment[J]. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 2018, 17(3): 15.