yahshibu/nested-ner-tacl2020
Implementation of Nested Named Entity Recognition
This project helps natural language processing (NLP) researchers and data scientists extract complex, overlapping entities from text. It takes raw text documents (like news articles or biomedical abstracts) and a pre-trained word embedding file as input, then identifies and labels nested named entities within the text. The output is a structured dataset showing all identified entities, even when one entity is contained within another. This is for researchers and data scientists working on advanced information extraction tasks.
No commits in the last 6 months.
Use this if you need to perform advanced information extraction from text where named entities can be nested (e.g., 'University of [California, Berkeley]' or '[New York] Times').
Not ideal if you only need to extract simple, non-overlapping named entities, or if you are looking for a ready-to-use application rather than a research implementation.
Stars
35
Forks
5
Language
Python
License
GPL-3.0
Category
Last pushed
Oct 29, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/yahshibu/nested-ner-tacl2020"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
charles9n/bert-sklearn
a sklearn wrapper for Google's BERT model
jidasheng/bi-lstm-crf
A PyTorch implementation of the BI-LSTM-CRF model.
howl-anderson/seq2annotation
基于 TensorFlow & PaddlePaddle 的通用序列标注算法库（目前包含 BiLSTM+CRF, Stacked-BiLSTM+CRF 和...
guillaumegenthial/tf_ner
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
guillaumegenthial/sequence_tagging
Named Entity Recognition (LSTM + CRF) - Tensorflow