DespinaChristou/REDSandT
Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings
This project helps researchers and data scientists automatically find and categorize relationships between entities in large text datasets, even when those relationships are subtle or less common. It takes raw text inputs, along with some enhanced information like entity types and structural paths, and outputs a broader set of accurately identified relations. This is useful for anyone working with unstructured text who needs to uncover connections, such as in scientific literature review or market intelligence.
No commits in the last 6 months.
Use this if you need to extract specific relationships between entities from large volumes of text, especially if existing methods struggle with less frequent or 'long-tail' relations.
Not ideal if you are looking for a ready-to-use, off-the-shelf application without any programming or data preparation involved.
Stars
24
Forks
4
Language
Python
License
Apache-2.0
Category
Last pushed
May 10, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/DespinaChristou/REDSandT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
chakki-works/seqeval
A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
Hironsan/anago
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
jbesomi/texthero
Text preprocessing, representation and visualization from zero to hero.
hamelsmu/ktext
Utilities for preprocessing text for deep learning with Keras
asahi417/tner
Language model fine-tuning on NER with an easy interface and cross-domain evaluation. "T-NER: An...