552 Open source projects that are alternatives of or similar to ChineseNER

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

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This repo contains a PyTorch implementation of a BiLSTM-CRF model for named entity recognition task.

Keras solution of Chinese NER task using BiLSTM-CRF/BiGRU-CRF/IDCNN-CRF model with Pretrained Language Model: supporting BERT/RoBERTa/ALBERT

텐서플로2와 머신러닝으로 시작하는 자연어처리 (로지스틱회귀부터 BERT와 GPT3까지) 실습자료

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

keras attentional bi-LSTM-CRF for Joint NLU (slot-filling and intent detection) with ATIS

参考NER，基于BERT的电商评论观点挖掘和情感分析

BiLSTM_CRF中文实体命名识别

Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.

面向中文电子病历的命名实体识别

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2019达观杯信息提取第5名代码

classy is a simple-to-use library for building high-performance Machine Learning models in NLP.

datagrand 2019 information extraction competition rank9

BiLSTM-CRF model for NER

reference tensorflow code for named entity tagging

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KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

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🎨 🎨NLP 自然语言处理教程 🎨🎨 https://dataxujing.github.io/NLP-paper/

从模型训练到部署，实战知识图谱(Knowledge Graph)&自然语言处理(NLP)。涉及 Tensorflow, Bert+Bi-LSTM+CRF,Neo4j等 涵盖 Named Entity Recognition,Text Classify,Information Extraction,Relation Extraction 等任务。

An Inplementation of CRF (Conditional Random Fields) in PyTorch 1.0

本NER项目包含多个中文数据集，模型采用BiLSTM+CRF、BERT+Softmax、BERT+Cascade、BERT+WOL等，最后用TFServing进行模型部署，线上推理和线下推理。

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中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

BERT-BiLSTM-CRF的Keras版实现

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Named Entity Recognition (LSTM + CRF + FastText) with models for [historic] German

天池比赛作品整理。实现从pdf中提取出姓名、出生年月、性别、电话、最高学历、籍贯、落户市县、政治面貌、毕业院校、工作单位、工作内容、职务、项目名称、项目责任、学位、毕业时间、工作时间、项目时间共18个字段。

KitanaQA: Adversarial training and data augmentation for neural question-answering models

命名体识别(NER)综述-论文-模型-代码(BiLSTM-CRF/BERT-CRF)-竞赛资源总结-随时更新

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Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。

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BERT-based joint intent detection and slot filling with intent-slot attention mechanism (INTERSPEECH 2021)

把李航老师《统计学习方法》的后几章的算法都用java实现了一遍，实现盒子与球的EM算法，扩展到去GMM训练，后来实现了HMM分词（实现了HMM分词的参数训练）和CRF分词（借用CRF++训练的参数模型），最后利用tensorFlow把BiLSTM+CRF实现了，然后为lucene包装了一个XinAnalyzer

keras+bi-lstm+crf，中文命名实体识别

LSTM-CRF for NER with ConLL-2002 dataset

An easy-to-use named entity recognition (NER) toolkit, implemented the Bi-LSTM+CRF model in tensorflow.

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

A Dataset of German Legal Documents for Named Entity Recognition

KU_NERDY 이동엽, 임희석 (2017 국어 정보 처리 시스템경진대회 금상) - 한글 및 한국어 정보처리 학술대회

法研杯CAIL2019阅读理解赛题参赛模型

reference pytorch code for named entity tagging

自然语言处理工具Macropodus，基于Albert+BiLSTM+CRF深度学习网络架构，中文分词，词性标注，命名实体识别，新词发现，关键词，文本摘要，文本相似度，科学计算器，中文数字阿拉伯数字(罗马数字)转换，中文繁简转换，拼音转换。tookit(tool) of NLP，CWS(chinese word segnment)，POS(Part-Of-Speech Tagging)，NER(name entity recognition)，Find(new words discovery)，Keyword(keyword extraction)，Summarize(text summarization)，Sim(text similarity)，Calculate(scientific calculator)，Chi2num(chinese number to arabic number)

pytorch实现bert做seq2seq任务，使用unilm方案,现在也可以做自动摘要，文本分类，情感分析，NER，词性标注等任务,支持GPT2进行文章续写。

Empower Sequence Labeling with Task-Aware Language Model

Code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs

This repository contains the official release of the model "BanglaBERT" and associated downstream finetuning code and datasets introduced in the paper titled "BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla" accpeted in Findings of the Annual Conference of the North American Chap…

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ACL 2018: Hybrid semi-Markov CRF for Neural Sequence Labeling (http://aclweb.org/anthology/P18-2038)

Chinese NER(Named Entity Recognition) using BERT(Softmax, CRF, Span)

Named Entity Recognition (LSTM + CRF) - Tensorflow