Popular GitHub Repositories for Parsing Expression Grammar

中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 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

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents

Unified framework for building enterprise RAG pipelines with small, specialized models

CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.

中文 NLP 预处理、解析工具包，准确、高效、易用 A Chinese NLP Preprocessing & Parsing Package www.jionlp.com

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Using modified BiSeNet for face parsing in PyTorch

??? Advanced NLP with spaCy: A free online course

A large-scale face dataset for face parsing, recognition, generation and editing.

A large collection of system log datasets for AI-driven log analytics [ISSRE'23]

A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset

Learn Japanese grammar with TypeScript

A machine learning toolkit for log parsing [ICSE'19, DSN'16]

A large annotated semantic parsing corpus for developing natural language interfaces.

Microsoft.Recognizers.Text provides recognition and resolution of numbers, units, date/time, etc. in multiple languages (ZH, EN, FR, ES, PT, DE, IT, TR, HI, NL. Partial support for JA, KO, AR, SV). Packages available at: https://www.nuget.org/profiles/Recognizers.Text, https://www.npmjs.com/~recognizers.text