possible1402's repositories
amazon-weak-ner-needle
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
bert
TensorFlow code and pre-trained models for BERT
CLUENER2020
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
concept-graphs
Concept embedding and network analysis of scientific innovations emergence
delphi
Code and examples for "Learning on Knowledge Graph Dynamics Provides Early Warning of Impactful Research".
flair
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Graphical_Models
Implementation of inference and learning algorithms for various PGMs without standard libraries. Includes projects from the PGM specialization on Coursera offered by Stanford University.
hunflair-experiments
Source code of the HunFlair experiments
joyful-pandas
pandas中文教程
machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
MedicalNamedEntityRecognition
Medical Named Entity Recognition implement using bi-directional lstm and crf model with char embedding.CCKS2017中文电子病例命名实体识别项目,主要实现使用了基于字向量的四层双向LSTM与CRF模型的网络.该项目提供了原始训练数据样本(一般醒目,出院情况,病史情况,病史特点,诊疗经过)与转换版本,训练脚本,预训练模型,可用于序列标注研究.把玩和PK使用.
Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL
Keras implementation of "Few-shot Learning for Named Entity Recognition in Medical Text"
named_entity_recognition
中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
nlp_chinese_corpus
大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
ProtoNER
Few-shot classification in Named Entity Recognition Task
python-project-blueprint
Blueprint/Boilerplate For Python Projects
subwordsLM
Unsupervised NER in chemical domain.
SuperalloyDigger
The functions of superalloyDigger toolkit include batch downloading documents in XML and TXT format from the Elsevier database, locating target sentences from the full text and automatically extracting triple information in the form of <material name, property specifier, value>.