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Explore all natural language processing open source software, libraries, packages, source code, cloud functions and APIs.

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of 'understanding' the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Popular New Releases in Natural Language Processing

transformers

v4.18.0: Checkpoint sharding, vision models

HanLP

v1.8.2 常规维护与准确率提升

spaCy

v3.1.6: Workaround for Click/Typer issues

flair

Release 0.11

allennlp

v2.9.2

transformers

v4.18.0: Checkpoint sharding, vision models

HanLP

v1.8.2 常规维护与准确率提升

spaCy

v3.1.6: Workaround for Click/Typer issues

flair

Release 0.11

allennlp

v2.9.2

Popular Libraries in Natural Language Processing

transformers

by huggingface python

star image 61400 Apache-2.0

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

funNLP

by fighting41love python

star image 33333

中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报告、自然语言生成、NLU太难了系列、自动对联数据及机器人、用户名黑名单列表、罪名法务名词及分类模型、微信公众号语料、cs224n深度学习自然语言处理课程、中文手写汉字识别、中文自然语言处理 语料/数据集、变量命名神器、分词语料库+代码、任务型对话英文数据集、ASR 语音数据集 + 基于深度学习的中文语音识别系统、笑声检测器、Microsoft多语言数字/单位/如日期时间识别包、中华新华字典数据库及api(包括常用歇后语、成语、词语和汉字)、文档图谱自动生成、SpaCy 中文模型、Common Voice语音识别数据集新版、神经网络关系抽取、基于bert的命名实体识别、关键词(Keyphrase)抽取包pke、基于医疗领域知识图谱的问答系统、基于依存句法与语义角色标注的事件三元组抽取、依存句法分析4万句高质量标注数据、cnocr:用来做中文OCR的Python3包、中文人物关系知识图谱项目、中文nlp竞赛项目及代码汇总、中文字符数据、speech-aligner: 从“人声语音”及其“语言文本”产生音素级别时间对齐标注的工具、AmpliGraph: 知识图谱表示学习(Python)库:知识图谱概念链接预测、Scattertext 文本可视化(python)、语言/知识表示工具:BERT & ERNIE、中文对比英文自然语言处理NLP的区别综述、Synonyms中文近义词工具包、HarvestText领域自适应文本挖掘工具(新词发现-情感分析-实体链接等)、word2word:(Python)方便易用的多语言词-词对集:62种语言/3,564个多语言对、语音识别语料生成工具:从具有音频/字幕的在线视频创建自动语音识别(ASR)语料库、构建医疗实体识别的模型(包含词典和语料标注)、单文档非监督的关键词抽取、Kashgari中使用gpt-2语言模型、开源的金融投资数据提取工具、文本自动摘要库TextTeaser: 仅支持英文、人民日报语料处理工具集、一些关于自然语言的基本模型、基于14W歌曲知识库的问答尝试--功能包括歌词接龙and已知歌词找歌曲以及歌曲歌手歌词三角关系的问答、基于Siamese bilstm模型的相似句子判定模型并提供训练数据集和测试数据集、用Transformer编解码模型实现的根据Hacker News文章标题自动生成评论、用BERT进行序列标记和文本分类的模板代码、LitBank:NLP数据集——支持自然语言处理和计算人文学科任务的100部带标记英文小说语料、百度开源的基准信息抽取系统、虚假新闻数据集、Facebook: LAMA语言模型分析,提供Transformer-XL/BERT/ELMo/GPT预训练语言模型的统一访问接口、CommonsenseQA:面向常识的英文QA挑战、中文知识图谱资料、数据及工具、各大公司内部里大牛分享的技术文档 PDF 或者 PPT、自然语言生成SQL语句(英文)、中文NLP数据增强(EDA)工具、英文NLP数据增强工具 、基于医药知识图谱的智能问答系统、京东商品知识图谱、基于mongodb存储的军事领域知识图谱问答项目、基于远监督的中文关系抽取、语音情感分析、中文ULMFiT-情感分析-文本分类-语料及模型、一个拍照做题程序、世界各国大规模人名库、一个利用有趣中文语料库 qingyun 训练出来的中文聊天机器人、中文聊天机器人seqGAN、省市区镇行政区划数据带拼音标注、教育行业新闻语料库包含自动文摘功能、开放了对话机器人-知识图谱-语义理解-自然语言处理工具及数据、中文知识图谱:基于百度百科中文页面-抽取三元组信息-构建中文知识图谱、masr: 中文语音识别-提供预训练模型-高识别率、Python音频数据增广库、中文全词覆盖BERT及两份阅读理解数据、ConvLab:开源多域端到端对话系统平台、中文自然语言处理数据集、基于最新版本rasa搭建的对话系统、基于TensorFlow和BERT的管道式实体及关系抽取、一个小型的证券知识图谱/知识库、复盘所有NLP比赛的TOP方案、OpenCLaP:多领域开源中文预训练语言模型仓库、UER:基于不同语料+编码器+目标任务的中文预训练模型仓库、中文自然语言处理向量合集、基于金融-司法领域(兼有闲聊性质)的聊天机器人、g2pC:基于上下文的汉语读音自动标记模块、Zincbase 知识图谱构建工具包、诗歌质量评价/细粒度情感诗歌语料库、快速转化「中文数字」和「阿拉伯数字」、百度知道问答语料库、基于知识图谱的问答系统、jieba_fast 加速版的jieba、正则表达式教程、中文阅读理解数据集、基于BERT等最新语言模型的抽取式摘要提取、Python利用深度学习进行文本摘要的综合指南、知识图谱深度学习相关资料整理、维基大规模平行文本语料、StanfordNLP 0.2.0:纯Python版自然语言处理包、NeuralNLP-NeuralClassifier:腾讯开源深度学习文本分类工具、端到端的封闭域对话系统、中文命名实体识别:NeuroNER vs. BertNER、新闻事件线索抽取、2019年百度的三元组抽取比赛:“科学空间队”源码、基于依存句法的开放域文本知识三元组抽取和知识库构建、中文的GPT2训练代码、ML-NLP - 机器学习(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM:Facebook的跨语言预训练语言模型、用基于BERT的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取、中文自然语言处理相关的开放任务-数据集-当前最佳结果、CoupletAI - 基于CNN+Bi-LSTM+Attention 的自动对对联系统、抽象知识图谱、MiningZhiDaoQACorpus - 580万百度知道问答数据挖掘项目、brat rapid annotation tool: 序列标注工具、大规模中文知识图谱数据:1.4亿实体、数据增强在机器翻译及其他nlp任务中的应用及效果、allennlp阅读理解:支持多种数据和模型、PDF表格数据提取工具 、 Graphbrain:AI开源软件库和科研工具,目的是促进自动意义提取和文本理解以及知识的探索和推断、简历自动筛选系统、基于命名实体识别的简历自动摘要、中文语言理解测评基准,包括代表性的数据集&基准模型&语料库&排行榜、树洞 OCR 文字识别 、从包含表格的扫描图片中识别表格和文字、语声迁移、Python口语自然语言处理工具集(英文)、 similarity:相似度计算工具包,java编写、海量中文预训练ALBERT模型 、Transformers 2.0 、基于大规模音频数据集Audioset的音频增强 、Poplar:网页版自然语言标注工具、图片文字去除,可用于漫画翻译 、186种语言的数字叫法库、Amazon发布基于知识的人-人开放领域对话数据集 、中文文本纠错模块代码、繁简体转换 、 Python实现的多种文本可读性评价指标、类似于人名/地名/组织机构名的命名体识别数据集 、东南大学《知识图谱》研究生课程(资料)、. 英文拼写检查库 、 wwsearch是企业微信后台自研的全文检索引擎、CHAMELEON:深度学习新闻推荐系统元架构 、 8篇论文梳理BERT相关模型进展与反思、DocSearch:免费文档搜索引擎、 LIDA:轻量交互式对话标注工具 、aili - the fastest in-memory index in the East 东半球最快并发索引 、知识图谱车音工作项目、自然语言生成资源大全 、中日韩分词库mecab的Python接口库、中文文本摘要/关键词提取、汉字字符特征提取器 (featurizer),提取汉字的特征(发音特征、字形特征)用做深度学习的特征、中文生成任务基准测评 、中文缩写数据集、中文任务基准测评 - 代表性的数据集-基准(预训练)模型-语料库-baseline-工具包-排行榜、PySS3:面向可解释AI的SS3文本分类器机器可视化工具 、中文NLP数据集列表、COPE - 格律诗编辑程序、doccano:基于网页的开源协同多语言文本标注工具 、PreNLP:自然语言预处理库、简单的简历解析器,用来从简历中提取关键信息、用于中文闲聊的GPT2模型:GPT2-chitchat、基于检索聊天机器人多轮响应选择相关资源列表(Leaderboards、Datasets、Papers)、(Colab)抽象文本摘要实现集锦(教程 、词语拼音数据、高效模糊搜索工具、NLP数据增广资源集、微软对话机器人框架 、 GitHub Typo Corpus:大规模GitHub多语言拼写错误/语法错误数据集、TextCluster:短文本聚类预处理模块 Short text cluster、面向语音识别的中文文本规范化、BLINK:最先进的实体链接库、BertPunc:基于BERT的最先进标点修复模型、Tokenizer:快速、可定制的文本词条化库、中文语言理解测评基准,包括代表性的数据集、基准(预训练)模型、语料库、排行榜、spaCy 医学文本挖掘与信息提取 、 NLP任务示例项目代码集、 python拼写检查库、chatbot-list - 行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍、语音质量评价指标(MOSNet, BSSEval, STOI, PESQ, SRMR)、 用138GB语料训练的法文RoBERTa预训练语言模型 、BERT-NER-Pytorch:三种不同模式的BERT中文NER实验、无道词典 - 有道词典的命令行版本,支持英汉互查和在线查询、2019年NLP亮点回顾、 Chinese medical dialogue data 中文医疗对话数据集 、最好的汉字数字(中文数字)-阿拉伯数字转换工具、 基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧、awesome-nlp-sentiment-analysis - 情感分析、情绪原因识别、评价对象和评价词抽取、LineFlow:面向所有深度学习框架的NLP数据高效加载器、中文医学NLP公开资源整理 、MedQuAD:(英文)医学问答数据集、将自然语言数字串解析转换为整数和浮点数、Transfer Learning in Natural Language Processing (NLP) 、面向语音识别的中文/英文发音辞典、Tokenizers:注重性能与多功能性的最先进分词器、CLUENER 细粒度命名实体识别 Fine Grained Named Entity Recognition、 基于BERT的中文命名实体识别、中文谣言数据库、NLP数据集/基准任务大列表、nlp相关的一些论文及代码, 包括主题模型、词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算等,涉及到各种与nlp相关的算法,基于keras和tensorflow 、Python文本挖掘/NLP实战示例、 Blackstone:面向非结构化法律文本的spaCy pipeline和NLP模型通过同义词替换实现文本“变脸” 、中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model 、albert-chinese-ner - 用预训练语言模型ALBERT做中文NER 、基于GPT2的特定主题文本生成/文本增广、开源预训练语言模型合集、多语言句向量包、编码、标记和实现:一种可控高效的文本生成方法、 英文脏话大列表 、attnvis:GPT2、BERT等transformer语言模型注意力交互可视化、CoVoST:Facebook发布的多语种语音-文本翻译语料库,包括11种语言(法语、德语、荷兰语、俄语、西班牙语、意大利语、土耳其语、波斯语、瑞典语、蒙古语和中文)的语音、文字转录及英文译文、Jiagu自然语言处理工具 - 以BiLSTM等模型为基础,提供知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类等功能、用unet实现对文档表格的自动检测,表格重建、NLP事件提取文献资源列表 、 金融领域自然语言处理研究资源大列表、CLUEDatasetSearch - 中英文NLP数据集:搜索所有中文NLP数据集,附常用英文NLP数据集 、medical_NER - 中文医学知识图谱命名实体识别 、(哈佛)讲因果推理的免费书、知识图谱相关学习资料/数据集/工具资源大列表、Forte:灵活强大的自然语言处理pipeline工具集 、Python字符串相似性算法库、PyLaia:面向手写文档分析的深度学习工具包、TextFooler:针对文本分类/推理的对抗文本生成模块、Haystack:灵活、强大的可扩展问答(QA)框架、中文关键短语抽取工具

bert

by google-research python

star image 28940 Apache-2.0

TensorFlow code and pre-trained models for BERT

jieba

by fxsjy python

star image 26924 MIT

结巴中文分词

Python

by geekcomputers python

star image 23653 MIT

My Python Examples

HanLP

by hankcs python

star image 23581 Apache-2.0

中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

spaCy

by explosion python

star image 23063 MIT

💫 Industrial-strength Natural Language Processing (NLP) in Python

fastText

by facebookresearch html

star image 22903 MIT

Library for fast text representation and classification.

NLP-progress

by sebastianruder python

star image 18988 MIT

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.

transformers

by huggingface python

star image 61400 Apache-2.0

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

funNLP

by fighting41love python

star image 33333

中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、历史名人词库、诗词词库、医学词库、饮食词库、法律词库、汽车词库、动物词库、中文聊天语料、中文谣言数据、百度中文问答数据集、句子相似度匹配算法集合、bert资源、文本生成&摘要相关工具、cocoNLP信息抽取工具、国内电话号码正则匹配、清华大学XLORE:中英文跨语言百科知识图谱、清华大学人工智能技术系列报告、自然语言生成、NLU太难了系列、自动对联数据及机器人、用户名黑名单列表、罪名法务名词及分类模型、微信公众号语料、cs224n深度学习自然语言处理课程、中文手写汉字识别、中文自然语言处理 语料/数据集、变量命名神器、分词语料库+代码、任务型对话英文数据集、ASR 语音数据集 + 基于深度学习的中文语音识别系统、笑声检测器、Microsoft多语言数字/单位/如日期时间识别包、中华新华字典数据库及api(包括常用歇后语、成语、词语和汉字)、文档图谱自动生成、SpaCy 中文模型、Common Voice语音识别数据集新版、神经网络关系抽取、基于bert的命名实体识别、关键词(Keyphrase)抽取包pke、基于医疗领域知识图谱的问答系统、基于依存句法与语义角色标注的事件三元组抽取、依存句法分析4万句高质量标注数据、cnocr:用来做中文OCR的Python3包、中文人物关系知识图谱项目、中文nlp竞赛项目及代码汇总、中文字符数据、speech-aligner: 从“人声语音”及其“语言文本”产生音素级别时间对齐标注的工具、AmpliGraph: 知识图谱表示学习(Python)库:知识图谱概念链接预测、Scattertext 文本可视化(python)、语言/知识表示工具:BERT & ERNIE、中文对比英文自然语言处理NLP的区别综述、Synonyms中文近义词工具包、HarvestText领域自适应文本挖掘工具(新词发现-情感分析-实体链接等)、word2word:(Python)方便易用的多语言词-词对集:62种语言/3,564个多语言对、语音识别语料生成工具:从具有音频/字幕的在线视频创建自动语音识别(ASR)语料库、构建医疗实体识别的模型(包含词典和语料标注)、单文档非监督的关键词抽取、Kashgari中使用gpt-2语言模型、开源的金融投资数据提取工具、文本自动摘要库TextTeaser: 仅支持英文、人民日报语料处理工具集、一些关于自然语言的基本模型、基于14W歌曲知识库的问答尝试--功能包括歌词接龙and已知歌词找歌曲以及歌曲歌手歌词三角关系的问答、基于Siamese bilstm模型的相似句子判定模型并提供训练数据集和测试数据集、用Transformer编解码模型实现的根据Hacker News文章标题自动生成评论、用BERT进行序列标记和文本分类的模板代码、LitBank:NLP数据集——支持自然语言处理和计算人文学科任务的100部带标记英文小说语料、百度开源的基准信息抽取系统、虚假新闻数据集、Facebook: LAMA语言模型分析,提供Transformer-XL/BERT/ELMo/GPT预训练语言模型的统一访问接口、CommonsenseQA:面向常识的英文QA挑战、中文知识图谱资料、数据及工具、各大公司内部里大牛分享的技术文档 PDF 或者 PPT、自然语言生成SQL语句(英文)、中文NLP数据增强(EDA)工具、英文NLP数据增强工具 、基于医药知识图谱的智能问答系统、京东商品知识图谱、基于mongodb存储的军事领域知识图谱问答项目、基于远监督的中文关系抽取、语音情感分析、中文ULMFiT-情感分析-文本分类-语料及模型、一个拍照做题程序、世界各国大规模人名库、一个利用有趣中文语料库 qingyun 训练出来的中文聊天机器人、中文聊天机器人seqGAN、省市区镇行政区划数据带拼音标注、教育行业新闻语料库包含自动文摘功能、开放了对话机器人-知识图谱-语义理解-自然语言处理工具及数据、中文知识图谱:基于百度百科中文页面-抽取三元组信息-构建中文知识图谱、masr: 中文语音识别-提供预训练模型-高识别率、Python音频数据增广库、中文全词覆盖BERT及两份阅读理解数据、ConvLab:开源多域端到端对话系统平台、中文自然语言处理数据集、基于最新版本rasa搭建的对话系统、基于TensorFlow和BERT的管道式实体及关系抽取、一个小型的证券知识图谱/知识库、复盘所有NLP比赛的TOP方案、OpenCLaP:多领域开源中文预训练语言模型仓库、UER:基于不同语料+编码器+目标任务的中文预训练模型仓库、中文自然语言处理向量合集、基于金融-司法领域(兼有闲聊性质)的聊天机器人、g2pC:基于上下文的汉语读音自动标记模块、Zincbase 知识图谱构建工具包、诗歌质量评价/细粒度情感诗歌语料库、快速转化「中文数字」和「阿拉伯数字」、百度知道问答语料库、基于知识图谱的问答系统、jieba_fast 加速版的jieba、正则表达式教程、中文阅读理解数据集、基于BERT等最新语言模型的抽取式摘要提取、Python利用深度学习进行文本摘要的综合指南、知识图谱深度学习相关资料整理、维基大规模平行文本语料、StanfordNLP 0.2.0:纯Python版自然语言处理包、NeuralNLP-NeuralClassifier:腾讯开源深度学习文本分类工具、端到端的封闭域对话系统、中文命名实体识别:NeuroNER vs. BertNER、新闻事件线索抽取、2019年百度的三元组抽取比赛:“科学空间队”源码、基于依存句法的开放域文本知识三元组抽取和知识库构建、中文的GPT2训练代码、ML-NLP - 机器学习(Machine Learning)NLP面试中常考到的知识点和代码实现、nlp4han:中文自然语言处理工具集(断句/分词/词性标注/组块/句法分析/语义分析/NER/N元语法/HMM/代词消解/情感分析/拼写检查、XLM:Facebook的跨语言预训练语言模型、用基于BERT的微调和特征提取方法来进行知识图谱百度百科人物词条属性抽取、中文自然语言处理相关的开放任务-数据集-当前最佳结果、CoupletAI - 基于CNN+Bi-LSTM+Attention 的自动对对联系统、抽象知识图谱、MiningZhiDaoQACorpus - 580万百度知道问答数据挖掘项目、brat rapid annotation tool: 序列标注工具、大规模中文知识图谱数据:1.4亿实体、数据增强在机器翻译及其他nlp任务中的应用及效果、allennlp阅读理解:支持多种数据和模型、PDF表格数据提取工具 、 Graphbrain:AI开源软件库和科研工具,目的是促进自动意义提取和文本理解以及知识的探索和推断、简历自动筛选系统、基于命名实体识别的简历自动摘要、中文语言理解测评基准,包括代表性的数据集&基准模型&语料库&排行榜、树洞 OCR 文字识别 、从包含表格的扫描图片中识别表格和文字、语声迁移、Python口语自然语言处理工具集(英文)、 similarity:相似度计算工具包,java编写、海量中文预训练ALBERT模型 、Transformers 2.0 、基于大规模音频数据集Audioset的音频增强 、Poplar:网页版自然语言标注工具、图片文字去除,可用于漫画翻译 、186种语言的数字叫法库、Amazon发布基于知识的人-人开放领域对话数据集 、中文文本纠错模块代码、繁简体转换 、 Python实现的多种文本可读性评价指标、类似于人名/地名/组织机构名的命名体识别数据集 、东南大学《知识图谱》研究生课程(资料)、. 英文拼写检查库 、 wwsearch是企业微信后台自研的全文检索引擎、CHAMELEON:深度学习新闻推荐系统元架构 、 8篇论文梳理BERT相关模型进展与反思、DocSearch:免费文档搜索引擎、 LIDA:轻量交互式对话标注工具 、aili - the fastest in-memory index in the East 东半球最快并发索引 、知识图谱车音工作项目、自然语言生成资源大全 、中日韩分词库mecab的Python接口库、中文文本摘要/关键词提取、汉字字符特征提取器 (featurizer),提取汉字的特征(发音特征、字形特征)用做深度学习的特征、中文生成任务基准测评 、中文缩写数据集、中文任务基准测评 - 代表性的数据集-基准(预训练)模型-语料库-baseline-工具包-排行榜、PySS3:面向可解释AI的SS3文本分类器机器可视化工具 、中文NLP数据集列表、COPE - 格律诗编辑程序、doccano:基于网页的开源协同多语言文本标注工具 、PreNLP:自然语言预处理库、简单的简历解析器,用来从简历中提取关键信息、用于中文闲聊的GPT2模型:GPT2-chitchat、基于检索聊天机器人多轮响应选择相关资源列表(Leaderboards、Datasets、Papers)、(Colab)抽象文本摘要实现集锦(教程 、词语拼音数据、高效模糊搜索工具、NLP数据增广资源集、微软对话机器人框架 、 GitHub Typo Corpus:大规模GitHub多语言拼写错误/语法错误数据集、TextCluster:短文本聚类预处理模块 Short text cluster、面向语音识别的中文文本规范化、BLINK:最先进的实体链接库、BertPunc:基于BERT的最先进标点修复模型、Tokenizer:快速、可定制的文本词条化库、中文语言理解测评基准,包括代表性的数据集、基准(预训练)模型、语料库、排行榜、spaCy 医学文本挖掘与信息提取 、 NLP任务示例项目代码集、 python拼写检查库、chatbot-list - 行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍、语音质量评价指标(MOSNet, BSSEval, STOI, PESQ, SRMR)、 用138GB语料训练的法文RoBERTa预训练语言模型 、BERT-NER-Pytorch:三种不同模式的BERT中文NER实验、无道词典 - 有道词典的命令行版本,支持英汉互查和在线查询、2019年NLP亮点回顾、 Chinese medical dialogue data 中文医疗对话数据集 、最好的汉字数字(中文数字)-阿拉伯数字转换工具、 基于百科知识库的中文词语多词义/义项获取与特定句子词语语义消歧、awesome-nlp-sentiment-analysis - 情感分析、情绪原因识别、评价对象和评价词抽取、LineFlow:面向所有深度学习框架的NLP数据高效加载器、中文医学NLP公开资源整理 、MedQuAD:(英文)医学问答数据集、将自然语言数字串解析转换为整数和浮点数、Transfer Learning in Natural Language Processing (NLP) 、面向语音识别的中文/英文发音辞典、Tokenizers:注重性能与多功能性的最先进分词器、CLUENER 细粒度命名实体识别 Fine Grained Named Entity Recognition、 基于BERT的中文命名实体识别、中文谣言数据库、NLP数据集/基准任务大列表、nlp相关的一些论文及代码, 包括主题模型、词向量(Word Embedding)、命名实体识别(NER)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算等,涉及到各种与nlp相关的算法,基于keras和tensorflow 、Python文本挖掘/NLP实战示例、 Blackstone:面向非结构化法律文本的spaCy pipeline和NLP模型通过同义词替换实现文本“变脸” 、中文 预训练 ELECTREA 模型: 基于对抗学习 pretrain Chinese Model 、albert-chinese-ner - 用预训练语言模型ALBERT做中文NER 、基于GPT2的特定主题文本生成/文本增广、开源预训练语言模型合集、多语言句向量包、编码、标记和实现:一种可控高效的文本生成方法、 英文脏话大列表 、attnvis:GPT2、BERT等transformer语言模型注意力交互可视化、CoVoST:Facebook发布的多语种语音-文本翻译语料库,包括11种语言(法语、德语、荷兰语、俄语、西班牙语、意大利语、土耳其语、波斯语、瑞典语、蒙古语和中文)的语音、文字转录及英文译文、Jiagu自然语言处理工具 - 以BiLSTM等模型为基础,提供知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类等功能、用unet实现对文档表格的自动检测,表格重建、NLP事件提取文献资源列表 、 金融领域自然语言处理研究资源大列表、CLUEDatasetSearch - 中英文NLP数据集:搜索所有中文NLP数据集,附常用英文NLP数据集 、medical_NER - 中文医学知识图谱命名实体识别 、(哈佛)讲因果推理的免费书、知识图谱相关学习资料/数据集/工具资源大列表、Forte:灵活强大的自然语言处理pipeline工具集 、Python字符串相似性算法库、PyLaia:面向手写文档分析的深度学习工具包、TextFooler:针对文本分类/推理的对抗文本生成模块、Haystack:灵活、强大的可扩展问答(QA)框架、中文关键短语抽取工具

bert

by google-research python

star image 28940 Apache-2.0

TensorFlow code and pre-trained models for BERT

jieba

by fxsjy python

star image 26924 MIT

结巴中文分词

Python

by geekcomputers python

star image 23653 MIT

My Python Examples

HanLP

by hankcs python

star image 23581 Apache-2.0

中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理

spaCy

by explosion python

star image 23063 MIT

💫 Industrial-strength Natural Language Processing (NLP) in Python

fastText

by facebookresearch html

star image 22903 MIT

Library for fast text representation and classification.

NLP-progress

by sebastianruder python

star image 18988 MIT

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.

Trending New libraries in Natural Language Processing

PaddleNLP

by PaddlePaddle python

star image 3119 Apache-2.0

Easy-to-use and Fast NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications.

NLP_ability

by DA-southampton python

star image 2488

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识,包括面试题,各种基础知识,工程能力等等,提升核心竞争力

texthero

by jbesomi python

star image 2212 MIT

Text preprocessing, representation and visualization from zero to hero.

gpt-neox

by EleutherAI python

star image 2012 Apache-2.0

An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

CLUEDatasetSearch

by CLUEbenchmark python

star image 1760

搜索所有中文NLP数据集,附常用英文NLP数据集

The-NLP-Pandect

by ivan-bilan python

star image 1418 CC0-1.0

A comprehensive reference for all topics related to Natural Language Processing

longformer

by allenai python

star image 1207 Apache-2.0

Longformer: The Long-Document Transformer

rebiber

by yuchenlin python

star image 1200 MIT

A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).

spago

by nlpodyssey go

star image 1116 BSD-2-Clause

Self-contained Machine Learning and Natural Language Processing library in Go

PaddleNLP

by PaddlePaddle python

star image 3119 Apache-2.0

Easy-to-use and Fast NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications.

NLP_ability

by DA-southampton python

star image 2488

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识,包括面试题,各种基础知识,工程能力等等,提升核心竞争力

texthero

by jbesomi python

star image 2212 MIT

Text preprocessing, representation and visualization from zero to hero.

gpt-neox

by EleutherAI python

star image 2012 Apache-2.0

An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

CLUEDatasetSearch

by CLUEbenchmark python

star image 1760

搜索所有中文NLP数据集,附常用英文NLP数据集

The-NLP-Pandect

by ivan-bilan python

star image 1418 CC0-1.0

A comprehensive reference for all topics related to Natural Language Processing

longformer

by allenai python

star image 1207 Apache-2.0

Longformer: The Long-Document Transformer

rebiber

by yuchenlin python

star image 1200 MIT

A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).

spago

by nlpodyssey go

star image 1116 BSD-2-Clause

Self-contained Machine Learning and Natural Language Processing library in Go

Top Authors in Natural Language Processing

1

allenai

43 Libraries

21327

2

IBM

40 Libraries

1095

3

microsoft

40 Libraries

13881

4

StarlangSoftware

37 Libraries

203

5

googlearchive

37 Libraries

6153

6

thunlp

35 Libraries

9769

7

facebookresearch

31 Libraries

41620

8

UKPLab

28 Libraries

8695

9

linuxscout

24 Libraries

739

10

undertheseanlp

22 Libraries

1302

1

43 Libraries

21327

2

40 Libraries

1095

3

40 Libraries

13881

4

37 Libraries

203

5

37 Libraries

6153

6

35 Libraries

9769

7

31 Libraries

41620

8

28 Libraries

8695

9

24 Libraries

739

10

22 Libraries

1302

Trending Kits in Natural Language Processing

Natural language processing is critical for developing intelligent systems. If you want to train your model faster and make it more relevant for users, developers must use data gathered from the real world. And for that, NLP libraries in Python are the most obvious choice. Python is one of the hottest programming languages across the globe because of its flexibility and features, and its ability to integrate with other languages. It is also highly acclaimed in the AI community and has grown to become one of the most sought-after languages for NLP (which, being a part of AI, relies heavily on machine learning). 

So, without any further ado, let’s take a look at some of the best Python libraries for natural language processing. Spacy is a professional-grade Python library for advanced NLP. Built on top of Python and Cython, it’s your no-frills go-to library for large-scale information extraction. Gensim is, again, one of the best Python libraries for natural language processing in terms of Topic Modelling. It offers memory-independent implementation capabilities and excels at retrieving information. With more than 47k stars on Github, Transformers offers thousands of pre-trained models to be implemented on texts for classification, translation, extraction, question answering, and summarizing in more than 100 languages. You can quickly download the APIs and start using them on any given text.

The following kit demonstrates how I used the NLP packages to carry the prediction with the news generated daily. In our day-to-day life, We hear a lot of news but may not be sure whether it's genuine or fake. Based on my model you can predict the news whether real or fake giving the high volume of data in a much shorter period of time. All you need is a High volume of Data that is classified into real or fake

Data Analysis

For Exploratory Data Analysis

Visualization

For Plotting and NLP

Machine Learning

For Machine learning and Github

This is a simple starter kit for developing virtual agents

Development Environment

Jupyter Notebook is a web based interactive environment often used for experiments.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation

Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics.

Text Mining

Text Mining Libraries

The provided code in python uses Multinomial Naive Bayes classifier for classification of the news articles as fake or real. MNB is a Bayesian learning approach. it is based on the Bayes theorem. After the model is built using the datasets (freely available online), there are two ways to check whether the news is true or fake. Firstly, a part of the article can be given as an input. Alternatively, the URL of the article can be given as an input. Images like "Fake News" or "Real News" will be displayed along with the corresponding text.

Group Name 1

Libraries used for working with arrays, analyzing data and visualization.

Group Name 2

Natural Language Toolkit is used to work with human language data, Scikit-learn provides efficient tools for machine learning, statistical and predictive requirements and various metrics (accuracy). Python is the high-level programming language used for achieving the goal.

Group Name 3

Wrapper, OCR tool, real-time optimized Computer Vision library tools.

NLP helps build systems that can automatically analyze, process, summarize and extract meaning from natural language text. In a nutshell, NLP helps machines mimic human behaviour and allows us to build applications that can reason about different types of documents. NLP open-source libraries are tools that allow you to build your own NLP applications. These libraries can be used to develop many different types of applications, like Speech Recognition, chatbots, Sentimental Analysis, Email Spam Filtering, Language Translator, search engines, and question answering systems. NLTK is one of the most popular NLP libraries in Python. It provides easy-to-use interfaces to corpora and lexical resources such as WordNet, along with statistical models for common tasks such as part-of-speech tagging and noun phrase extraction. Following list has libraries for most basic Sentimental Analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) Tool, collection of NLP resources – blogs, books, tutorials, and more. Check out the list of free, open source libraries to help you with your projects:

Some Popular Open Source Libraries to get you started

Utilize the below libraries to tokenize, implement part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition.

Sentimental Analysis Repository

Some interesting courses to Deep Dive

1. List of Popular Courses on NLP 2. Stanford Course on Natural Language Processing 3. André Ribeiro Miranda- NLP Course

Recording from Session on Build AI fake News Detector

Watch recording of a live training session on AI Fake News Detection

Example project on AI Virtual Agent that you can build in 30 mins

Here's a project with the installer, source code, and step-by-step tutorial that you can build in under 30 mins. ⬇️Get the 1-Click install AI Virtual Agent kit Watch recording of a live training session on AI Virtual Agent

Virtual Agents have gained popularity due to the advancement of technologies in the area of Artificial Intelligence and Natural Language Understanding. They've become inevitable these days, particularly in support department of businesses as it can serve customers with quick turnaround. This kit aids rapid development of Virtual Agents by following below steps. 1. Select a development environment of your choice 2. Explore and analyse the dataset 3. Cleanse and get the noise-free data 4. Compute embeddings for the dataset - sentence or word embeddings 5. Preprocess the user query 6. Compute embeddings for user query 7. Compare and compute similarity score to find a best match 8. Look up the dataset for displaying answer of a best matched query 9. Precomputed embeddings can be persisted for later use

Environment Used

Jupyter Notebook is used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms

Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics.

Virtual Agents built

FAQ Virtual Agents created using this kit are added in this section.

This starter kit has all the required kits/libraries for creating your own virtual assistant. It contains various kits for getting started such as follows: 1. Development environment 2. Exploratory data analysis 3. Machine learning 4. Text Mining 5. NLP - sentence embedding, cosine similarities

Development Environment

Jupyter notebook and vscode are used for development and are known as IDEs. To write any code it is necessary to have an development environment setup. Jupyter notebook is web based interactive environment.

Exploratory data analysis

Libraries that deal with arrays and help in data analysis for data engineering. Arrays can be manipulated meaning the dimensions, reshaping, etc.

Machine Learning

Basic machine learning libraries that creates a model and also trains the model using the dataset. Also is used for prediction purposes to test whether model trained is accurate or not.

Exploratory data analysis

Libraries which are used for analysis and processing of unprocessed natural language.

NLP

Libraries in this group are used to clean the dataset by removing all punctuations, digits, symbols, etc. After data preprocessing, the user query is compared with dataset queries via cosine similarity algorithm which will give us the record in dataset which is similar to user query.

Natural Language Processing (NLP) is a broad subject that falls under the Artificial Intelligence (AI) domain. NLP allows computers to interpret text and spoken language in the same way that people do. NLP must be able to grasp not only words, but also phrases and paragraphs in their context based on syntax, grammar, and other factors. NLP algorithms break down human speech into machine-understandable fragments that can be utilized to create NLP-based software.

Because of the development of useful NLP libraries, NLP is now finding applications across a wide range of industries. NLP has become a critical component of Deep Learning development. Among other NLP applications, extracting useful information from text is crucial for building chatbots and virtual assistants, among other NLP applications, because training NLP algorithms require a large amount of data for better performance, but our Google Assistant and Alexa are becoming more natural by the day. Here are some basic libraries to get started with NLP.

NLTK Natural Language Toolkit is one of the most frequently used libraries in the industry for building Python applications that interact with human language data. NLTK can assist you with anything from splitting sentences from paragraphs to recognizing the part of speech of specific phrases to emphasizing the primary theme. It is a highly important tool for preparing text for future analysis, such as when using Models. It assists in the translation of words into numbers, with which the model may subsequently function. This collection contains nearly all of the tools required for NLP. It helps with text classification, tokenization, parsing, part-of-speech tagging and stemming. spaCy spaCy is a python library built for sophisticated Natural Language Processing. It is based on cutting-edge research and was intended from the start to be utilized in real-world products. spaCy has pre-trained pipelines and presently supports tokenization and training for more than 60 languages. It includes cutting-edge speed and neural network models for tagging, parsing, named entity identification, text classification, and other tasks, as well as a production-ready training system and simple model packaging, deployment, and workflow management. Gensim Gensim is a well-known Python package for doing natural language processing tasks. It has a unique feature that uses vector space modeling and topic modeling tools to determine the semantic similarity between two documents.

CoreNLP CoreNLP can be used to create linguistic annotations for text, such as Token and sentence boundaries, Parts of speech, Named entities, Numeric and temporal values, dependency and constituency parser, Sentiment, Quotation attributions, and Relations between words. CoreNLP supports a variety of Human languages such as Arabic, Chinese, English, French, German, and Spanish. It is written in Java but has support for Python as well. Pattern Pattern is a python based NLP library that provides features such as part-of-speech tagging, sentiment analysis, and vector space modeling. It offers support for Twitter and Facebook APIs, a DOM parser, and a web crawler. Pattern is often used to convert HTML data to plain text and resolve spelling mistakes in textual data. Polyglot Polyglot library provides an impressive breadth of analysis and covers a wide range of languages. Polyglot's SpaCy-like efficiency and ease of use make it an excellent choice for projects that need a language that SpaCy does not support. The polyglot package provides a command-line interface as well as library access through pipeline methods.

TextBlob TextBlob is a python library that is often used for natural language processing (NLP) tasks such as voice tagging, noun phrase extraction, sentiment analysis, and classification. This library is based on the NLTK library. Its user-friendly interface provides access to basic NLP tasks such as sentiment analysis, word extraction, parsing, and many more. Flair Flair supports an increasing number of languages, you may apply the latest NLP models to your text, such as named entity recognition, part-of-speech tagging, and classification, as well as sense disambiguation and classification. It is a deep learning library built on top of PyTorch for NLP tasks. Flair natively provides pre-trained models for NLP tasks such asText classification, Part-of-Speech tagging and Name Entity Recognition

This kit helps in making a FAQ virtual agent within a small amount of time with the given libraries. 1) py-lingualytics - A text analytics library with support for codemixed data 2) faq-virtual-agent - Guides in making the VA with the help of some questions related to kandi which can be used 3) sentence-transformers - Multilingual Sentence & Image Embeddings with BERT 4) bert-cosine-sim - Fine-tune BERT to generate sentence embedding for cosine similarity

Source Kit

Machine Learning

𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝑨𝒈𝒆𝒏𝒕𝒔 are those computer programs that are a mix of programmed rules and conversational intelligence in providing basic help. They contain chatbots, voice bots, and even interactive voice responses. It is termed as a 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒂𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕. Virtual agents can communicate through any medium. A 𝒗𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕 is a remote customer service that uses technology to provide service without the need to directly interact with the company's agent.

  • 𝑺𝒐𝒎𝒆 𝒃𝒆𝒏𝒆𝒇𝒊𝒕𝒔 𝒐𝒇 𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒈𝒆𝒏𝒕𝒔 𝒂𝒓𝒆:

𝑻𝒚𝒑𝒆𝒔 𝒐𝒇 𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒈𝒆𝒏𝒕

The Virtual agents are classified into different types based on the media of communication:

𝟏) 𝑽𝒐𝒊𝒄𝒆 𝑩𝒐𝒕𝒔

Voice bots are software developed by artificial intelligence that allows a caller to navigate an interactive voice response system with their voice, generally using natural language

𝟐) 𝑪𝒉𝒂𝒕𝒃𝒐𝒕𝒔

A chatbot is a software application used to conduct an online chat conversation via text or text-to-speech. It is consistent and allows 24/7 customer support.

𝟑) 𝑰𝒏𝒕𝒆𝒓𝒂𝒄𝒕𝒊𝒗𝒆 𝑩𝒐𝒕𝒔

Interactive Bots are a form of Chatbot where the conversational flow is driven by Symphony Elements. Instead of listening for plain text as the only source of data, Interactive Bots can collect data or commands through structured forms.

A virtualreplyagent Example Kit, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes tasks for the user. This kit aids the rapid development of Virtual Agents by following the below steps. 1 Select a development environment of your choice 2. Explore and analyse the dataset 3. Cleanse and get the noise-free data & Compute embeddings for the dataset-sentence or word embeddings 5. Preprocess the user query 6 Compute embeddings for user query 7. Compare and compute similarity score to find the best match & Look up the dataset for displaying answers to a best-matched query 3. Precomputed embeddings can be persisted for later use 10. Servers and web frameworks can be leveraged for servicing the request as REST API

This kit consists of a virtual agent for FAQ. We have used jupyter notebook and also py lingualytics library and cosine similarity library for sentence transform.

Sentencte Transformers

Jupyter

Paraphrasing refers to rewriting something in different words and using different expressions. It does not include changing the whole concept or meaning. It is a method in which we use words’ alternatives and different sentence structures. Paraphrasing is a restatement of any content or text. This is done by using a sentence re-phraser (Paraphraser). What is a good paraphrase? Almost all conditioned text generation models are validated on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct English (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But a good paraphrase should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the 3 key metrics that measures the quality of paraphrases are:

  • Adequacy (Is the meaning preserved adequately?)
  • Fluency (Is the paraphrase fluent English?)
  • Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
The aim of a paraphraser is to create paraphrases that are fluent and have the same meaning. There are many uses or applications of a Paraphraser:
  • Data Augmentation: Paraphrasing helps in augmenting/creating training data for Natural Language Understanding(NLU) models to build robust models for conversational engines by creating equivalent paraphrases for a particular phrase or sentence thereby creating a text corpus as training data.
  • Summarization: Paraphrasing helps to create summaries of a large text corpus for understanding the crux of the text corpus.
  • Sentence Rephrasing: Paraphrasing helps in generating sentences with similar context for a particular phrase/sentence. These rephrased sentences can be used to create plagiarism free content for articles, blogs etc.
  • A typical process flow to create training data by data augmentation using Paraphraser is picturized below:

Group Name 1

Troubleshooting

For Windows users: While you attempt to run the kit_installer batch file, you might be view a prompt from Microsoft Defender as below:

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.

Group Name 1

This is a basic kit for a Virtual Assistant with elements such as development environment, data analysis, exploration frameworks and sentence embedding.

This is a quick start demo kit with basic elements of a Virtual Assistant such as a development environment, data analysis and exploration frameworks, and sentence embedding.

This is a Quick Start demo kit with basic elements of Virtual Assistance such as a development environment,data analysis and exploration frameworks and sentence embedding.

This is a Quick Start demo kit with basic elements of a Virtual Assistant such as a development environment, data analysis and exploration frameworks and sentence embedding.

This is a quick start demo kit with basic elements of a virtual assistant such as development environment, data analysis and exploration frameworks and sentence embedding

Did you ever wish to have a personalized Google assistant or Siri to support your own business? This kit helps you achieve exactly the same in few simple steps by using inbuilt libraries.

Natural language processing is critical for developing intelligent systems. If you want to train your model faster and make it more relevant for users, developers must use data gathered from the real world. And for that, NLP libraries in Python are the most obvious choice. Python is one of the hottest programming languages across the globe because of its flexibility and features, and its ability to integrate with other languages. It is also highly acclaimed in the AI community and has grown to become one of the most sought-after languages for NLP (which, being a part of AI, relies heavily on machine learning). 

So, without any further ado, let’s take a look at some of the best Python libraries for natural language processing. Spacy is a professional-grade Python library for advanced NLP. Built on top of Python and Cython, it’s your no-frills go-to library for large-scale information extraction. Gensim is, again, one of the best Python libraries for natural language processing in terms of Topic Modelling. It offers memory-independent implementation capabilities and excels at retrieving information. With more than 47k stars on Github, Transformers offers thousands of pre-trained models to be implemented on texts for classification, translation, extraction, question answering, and summarizing in more than 100 languages. You can quickly download the APIs and start using them on any given text.

The following kit demonstrates how I used the NLP packages to carry the prediction with the news generated daily. In our day-to-day life, We hear a lot of news but may not be sure whether it's genuine or fake. Based on my model you can predict the news whether real or fake giving the high volume of data in a much shorter period of time. All you need is a High volume of Data that is classified into real or fake

Data Analysis

For Exploratory Data Analysis

Visualization

For Plotting and NLP

Machine Learning

For Machine learning and Github

This is a simple starter kit for developing virtual agents

Development Environment

Jupyter Notebook is a web based interactive environment often used for experiments.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation

Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics.

Text Mining

Text Mining Libraries

The provided code in python uses Multinomial Naive Bayes classifier for classification of the news articles as fake or real. MNB is a Bayesian learning approach. it is based on the Bayes theorem. After the model is built using the datasets (freely available online), there are two ways to check whether the news is true or fake. Firstly, a part of the article can be given as an input. Alternatively, the URL of the article can be given as an input. Images like "Fake News" or "Real News" will be displayed along with the corresponding text.

Group Name 1

Libraries used for working with arrays, analyzing data and visualization.

Group Name 2

Natural Language Toolkit is used to work with human language data, Scikit-learn provides efficient tools for machine learning, statistical and predictive requirements and various metrics (accuracy). Python is the high-level programming language used for achieving the goal.

Group Name 3

Wrapper, OCR tool, real-time optimized Computer Vision library tools.

NLP helps build systems that can automatically analyze, process, summarize and extract meaning from natural language text. In a nutshell, NLP helps machines mimic human behaviour and allows us to build applications that can reason about different types of documents. NLP open-source libraries are tools that allow you to build your own NLP applications. These libraries can be used to develop many different types of applications, like Speech Recognition, chatbots, Sentimental Analysis, Email Spam Filtering, Language Translator, search engines, and question answering systems. NLTK is one of the most popular NLP libraries in Python. It provides easy-to-use interfaces to corpora and lexical resources such as WordNet, along with statistical models for common tasks such as part-of-speech tagging and noun phrase extraction. Following list has libraries for most basic Sentimental Analysis VADER (Valence Aware Dictionary and sEntiment Reasoner) Tool, collection of NLP resources – blogs, books, tutorials, and more. Check out the list of free, open source libraries to help you with your projects:

Some Popular Open Source Libraries to get you started

Utilize the below libraries to tokenize, implement part-of-speech tagging, stemming, sentiment analysis, topic segmentation, and named entity recognition.

Sentimental Analysis Repository

Some interesting courses to Deep Dive

1. List of Popular Courses on NLP 2. Stanford Course on Natural Language Processing 3. André Ribeiro Miranda- NLP Course

Recording from Session on Build AI fake News Detector

Watch recording of a live training session on AI Fake News Detection

Example project on AI Virtual Agent that you can build in 30 mins

Here's a project with the installer, source code, and step-by-step tutorial that you can build in under 30 mins. ⬇️Get the 1-Click install AI Virtual Agent kit Watch recording of a live training session on AI Virtual Agent

Virtual Agents have gained popularity due to the advancement of technologies in the area of Artificial Intelligence and Natural Language Understanding. They've become inevitable these days, particularly in support department of businesses as it can serve customers with quick turnaround. This kit aids rapid development of Virtual Agents by following below steps. 1. Select a development environment of your choice 2. Explore and analyse the dataset 3. Cleanse and get the noise-free data 4. Compute embeddings for the dataset - sentence or word embeddings 5. Preprocess the user query 6. Compute embeddings for user query 7. Compare and compute similarity score to find a best match 8. Look up the dataset for displaying answer of a best matched query 9. Precomputed embeddings can be persisted for later use

Environment Used

Jupyter Notebook is used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms

Machine Learning

Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics.

Virtual Agents built

FAQ Virtual Agents created using this kit are added in this section.

This starter kit has all the required kits/libraries for creating your own virtual assistant. It contains various kits for getting started such as follows: 1. Development environment 2. Exploratory data analysis 3. Machine learning 4. Text Mining 5. NLP - sentence embedding, cosine similarities

Development Environment

Jupyter notebook and vscode are used for development and are known as IDEs. To write any code it is necessary to have an development environment setup. Jupyter notebook is web based interactive environment.

Exploratory data analysis

Libraries that deal with arrays and help in data analysis for data engineering. Arrays can be manipulated meaning the dimensions, reshaping, etc.

Machine Learning

Basic machine learning libraries that creates a model and also trains the model using the dataset. Also is used for prediction purposes to test whether model trained is accurate or not.

Exploratory data analysis

Libraries which are used for analysis and processing of unprocessed natural language.

NLP

Libraries in this group are used to clean the dataset by removing all punctuations, digits, symbols, etc. After data preprocessing, the user query is compared with dataset queries via cosine similarity algorithm which will give us the record in dataset which is similar to user query.

Natural Language Processing (NLP) is a broad subject that falls under the Artificial Intelligence (AI) domain. NLP allows computers to interpret text and spoken language in the same way that people do. NLP must be able to grasp not only words, but also phrases and paragraphs in their context based on syntax, grammar, and other factors. NLP algorithms break down human speech into machine-understandable fragments that can be utilized to create NLP-based software.

Because of the development of useful NLP libraries, NLP is now finding applications across a wide range of industries. NLP has become a critical component of Deep Learning development. Among other NLP applications, extracting useful information from text is crucial for building chatbots and virtual assistants, among other NLP applications, because training NLP algorithms require a large amount of data for better performance, but our Google Assistant and Alexa are becoming more natural by the day. Here are some basic libraries to get started with NLP.

NLTK Natural Language Toolkit is one of the most frequently used libraries in the industry for building Python applications that interact with human language data. NLTK can assist you with anything from splitting sentences from paragraphs to recognizing the part of speech of specific phrases to emphasizing the primary theme. It is a highly important tool for preparing text for future analysis, such as when using Models. It assists in the translation of words into numbers, with which the model may subsequently function. This collection contains nearly all of the tools required for NLP. It helps with text classification, tokenization, parsing, part-of-speech tagging and stemming. spaCy spaCy is a python library built for sophisticated Natural Language Processing. It is based on cutting-edge research and was intended from the start to be utilized in real-world products. spaCy has pre-trained pipelines and presently supports tokenization and training for more than 60 languages. It includes cutting-edge speed and neural network models for tagging, parsing, named entity identification, text classification, and other tasks, as well as a production-ready training system and simple model packaging, deployment, and workflow management. Gensim Gensim is a well-known Python package for doing natural language processing tasks. It has a unique feature that uses vector space modeling and topic modeling tools to determine the semantic similarity between two documents.

CoreNLP CoreNLP can be used to create linguistic annotations for text, such as Token and sentence boundaries, Parts of speech, Named entities, Numeric and temporal values, dependency and constituency parser, Sentiment, Quotation attributions, and Relations between words. CoreNLP supports a variety of Human languages such as Arabic, Chinese, English, French, German, and Spanish. It is written in Java but has support for Python as well. Pattern Pattern is a python based NLP library that provides features such as part-of-speech tagging, sentiment analysis, and vector space modeling. It offers support for Twitter and Facebook APIs, a DOM parser, and a web crawler. Pattern is often used to convert HTML data to plain text and resolve spelling mistakes in textual data. Polyglot Polyglot library provides an impressive breadth of analysis and covers a wide range of languages. Polyglot's SpaCy-like efficiency and ease of use make it an excellent choice for projects that need a language that SpaCy does not support. The polyglot package provides a command-line interface as well as library access through pipeline methods.

TextBlob TextBlob is a python library that is often used for natural language processing (NLP) tasks such as voice tagging, noun phrase extraction, sentiment analysis, and classification. This library is based on the NLTK library. Its user-friendly interface provides access to basic NLP tasks such as sentiment analysis, word extraction, parsing, and many more. Flair Flair supports an increasing number of languages, you may apply the latest NLP models to your text, such as named entity recognition, part-of-speech tagging, and classification, as well as sense disambiguation and classification. It is a deep learning library built on top of PyTorch for NLP tasks. Flair natively provides pre-trained models for NLP tasks such asText classification, Part-of-Speech tagging and Name Entity Recognition

This kit helps in making a FAQ virtual agent within a small amount of time with the given libraries. 1) py-lingualytics - A text analytics library with support for codemixed data 2) faq-virtual-agent - Guides in making the VA with the help of some questions related to kandi which can be used 3) sentence-transformers - Multilingual Sentence & Image Embeddings with BERT 4) bert-cosine-sim - Fine-tune BERT to generate sentence embedding for cosine similarity

Source Kit

Machine Learning

𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝑨𝒈𝒆𝒏𝒕𝒔 are those computer programs that are a mix of programmed rules and conversational intelligence in providing basic help. They contain chatbots, voice bots, and even interactive voice responses. It is termed as a 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒂𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕. Virtual agents can communicate through any medium. A 𝒗𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕 is a remote customer service that uses technology to provide service without the need to directly interact with the company's agent.

  • 𝑺𝒐𝒎𝒆 𝒃𝒆𝒏𝒆𝒇𝒊𝒕𝒔 𝒐𝒇 𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒈𝒆𝒏𝒕𝒔 𝒂𝒓𝒆:

𝑻𝒚𝒑𝒆𝒔 𝒐𝒇 𝑽𝒊𝒓𝒕𝒖𝒂𝒍 𝒂𝒈𝒆𝒏𝒕

The Virtual agents are classified into different types based on the media of communication:

𝟏) 𝑽𝒐𝒊𝒄𝒆 𝑩𝒐𝒕𝒔

Voice bots are software developed by artificial intelligence that allows a caller to navigate an interactive voice response system with their voice, generally using natural language

𝟐) 𝑪𝒉𝒂𝒕𝒃𝒐𝒕𝒔

A chatbot is a software application used to conduct an online chat conversation via text or text-to-speech. It is consistent and allows 24/7 customer support.

𝟑) 𝑰𝒏𝒕𝒆𝒓𝒂𝒄𝒕𝒊𝒗𝒆 𝑩𝒐𝒕𝒔

Interactive Bots are a form of Chatbot where the conversational flow is driven by Symphony Elements. Instead of listening for plain text as the only source of data, Interactive Bots can collect data or commands through structured forms.

A virtualreplyagent Example Kit, also called an AI assistant or digital assistant, is an application program that understands natural language voice commands and completes tasks for the user. This kit aids the rapid development of Virtual Agents by following the below steps. 1 Select a development environment of your choice 2. Explore and analyse the dataset 3. Cleanse and get the noise-free data & Compute embeddings for the dataset-sentence or word embeddings 5. Preprocess the user query 6 Compute embeddings for user query 7. Compare and compute similarity score to find the best match & Look up the dataset for displaying answers to a best-matched query 3. Precomputed embeddings can be persisted for later use 10. Servers and web frameworks can be leveraged for servicing the request as REST API

This kit consists of a virtual agent for FAQ. We have used jupyter notebook and also py lingualytics library and cosine similarity library for sentence transform.

Sentencte Transformers

Jupyter

Paraphrasing refers to rewriting something in different words and using different expressions. It does not include changing the whole concept or meaning. It is a method in which we use words’ alternatives and different sentence structures. Paraphrasing is a restatement of any content or text. This is done by using a sentence re-phraser (Paraphraser). What is a good paraphrase? Almost all conditioned text generation models are validated on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct English (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But a good paraphrase should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the 3 key metrics that measures the quality of paraphrases are:

  • Adequacy (Is the meaning preserved adequately?)
  • Fluency (Is the paraphrase fluent English?)
  • Diversity (Lexical / Phrasal / Syntactical) (How much has the paraphrase changed the original sentence?)
The aim of a paraphraser is to create paraphrases that are fluent and have the same meaning. There are many uses or applications of a Paraphraser:
  • Data Augmentation: Paraphrasing helps in augmenting/creating training data for Natural Language Understanding(NLU) models to build robust models for conversational engines by creating equivalent paraphrases for a particular phrase or sentence thereby creating a text corpus as training data.
  • Summarization: Paraphrasing helps to create summaries of a large text corpus for understanding the crux of the text corpus.
  • Sentence Rephrasing: Paraphrasing helps in generating sentences with similar context for a particular phrase/sentence. These rephrased sentences can be used to create plagiarism free content for articles, blogs etc.
  • A typical process flow to create training data by data augmentation using Paraphraser is picturized below:

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Troubleshooting

For Windows users: While you attempt to run the kit_installer batch file, you might be view a prompt from Microsoft Defender as below:

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.

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This is a basic kit for a Virtual Assistant with elements such as development environment, data analysis, exploration frameworks and sentence embedding.

This is a quick start demo kit with basic elements of a Virtual Assistant such as a development environment, data analysis and exploration frameworks, and sentence embedding.

This is a Quick Start demo kit with basic elements of Virtual Assistance such as a development environment,data analysis and exploration frameworks and sentence embedding.

This is a Quick Start demo kit with basic elements of a Virtual Assistant such as a development environment, data analysis and exploration frameworks and sentence embedding.

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QUESTION

How can I convert this language to actual numbers and text?

Asked 2022-Mar-06 at 13:11

I am working on natural language processing project with deep learning and I downloaded a word embedding file. The file is in .bin format. I can open that file with

1file = open("cbow.bin", "rb")
2

But when I type

1file = open("cbow.bin", "rb")
2file.read(100)
3

I get

1file = open("cbow.bin", "rb")
2file.read(100)
3b'4347907 300\n</s> H\xe1\xae:0\x16\xc1:\xbfX\xa7\xbaR8\x8f\xba\xa0\xd3\xee9K\xfe\x83::m\xa49\xbc\xbb\x938\xa4p\x9d\xbat\xdaA:UU\xbe\xba\x93_\xda9\x82N\x83\xb9\xaeG\xa7\xb9\xde\xdd\x90\xbaww$\xba\xfdba:\x14.\x84:R\xb8\x81:0\x96\x0b:\x96\xfc\x06'  
4

What is this language and How can I convert it into actual numbers and text using python?

ANSWER

Answered 2022-Mar-06 at 13:11

This weird language you are referring to is a python bytestring.

As @jolitti implied that you won't be able to convert this particular bytestring to readable text.

If the bytestring contained any characters you recognize then would have been displayed like this.

1file = open("cbow.bin", "rb")
2file.read(100)
3b'4347907 300\n</s> H\xe1\xae:0\x16\xc1:\xbfX\xa7\xbaR8\x8f\xba\xa0\xd3\xee9K\xfe\x83::m\xa49\xbc\xbb\x938\xa4p\x9d\xbat\xdaA:UU\xbe\xba\x93_\xda9\x82N\x83\xb9\xaeG\xa7\xb9\xde\xdd\x90\xbaww$\xba\xfdba:\x14.\x84:R\xb8\x81:0\x96\x0b:\x96\xfc\x06'  
4b'Guido van Rossum'
5

Source https://stackoverflow.com/questions/71370347