taishan1994/BERT-Relation-Extraction
使用bert进行关系三元组抽取。
This tool helps you extract key information and relationships from Chinese text, like finding all the 'faulty parts' and 'failure reasons' in a car repair log, and then linking which 'reason' caused a 'part' to 'fail'. It takes your raw Chinese text as input and outputs structured data showing these extracted entities and the relationships between them. This is useful for anyone who needs to quickly pinpoint specific items and their connections within large volumes of domain-specific Chinese documents.
184 stars. No commits in the last 6 months.
Use this if you need to automatically identify specific entities (like product names, people, or problem descriptions) and the relationships between them within unstructured Chinese text from technical reports, news articles, or other specialized documents.
Not ideal if your primary goal is general text summarization, sentiment analysis, or if your documents are not in Chinese.
Stars
184
Forks
18
Language
Python
License
—
Category
Last pushed
Apr 09, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/taishan1994/BERT-Relation-Extraction"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
davidsbatista/BREDS
"Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) in...
davidsbatista/Snowball
Implementation with some extensions of the paper "Snowball: Extracting Relations from Large...
nicolay-r/AREkit
Document level Attitude and Relation Extraction toolkit (AREkit) for sampling and processing...
plkmo/BERT-Relation-Extraction
PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper
thunlp/FewRel
A Large-Scale Few-Shot Relation Extraction Dataset