/open-entity-relation-extraction

Chinese Open Entity Relation Extraction

Primary LanguagePythonMIT LicenseMIT

Chinese Open Entity Relation Extraction

The content of this work is to extract entity relations based on dependency syntax for open domain.

Welcome to watch, star or fork.

Extraction Example

"**国家主席***访问韩国，并在首尔大学发表演讲"

We can extract knowledge triples from the sentence as follows:

  • (**, 国家主席, ***)
  • (***, 访问, 韩国)
  • (***, 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
|-- ltp-models/  # 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:

  • pynlpir>=0.5.2
  • pyltp>=0.2.1

Install Dependent libraries

pip install -r requirements.txt

Entry procedure

cd ./code/demo/
python extract_demo.py

Main Implementation Content

DSNF

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.