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dependency-parsing 开源项目推荐(151 个)
dependency-parsing 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 151 个仓库百度开源的依存句法分析系统
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
🚀 State-of-the-art parsers for natural language.
👑 spaCy building blocks and visualizers for Streamlit apps
基于Pytorch和torchtext的自然语言处理深度学习框架。
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
A Vietnamese natural language processing toolkit (NAACL 2018)
Natural Language Processing Pipeline - Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging and Dependency Parsing
CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.
Deep neural models for core NLP tasks (Pytorch version)
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (authors' PyTorch implementation)
[ACL-IJCNLP 2021] Automated Concatenation of Embeddings for Structured Prediction
State-of-the-art, lightweight NLP tools for Turkish language. Developed by VNGRS.
NLP速查表、Python、spacy、LexNPL、NLTK、分词、词干提取、句子检测、命名实体识别
HuSpaCy: industrial-strength Hungarian natural language processing
A minimal, pure Python library to interface with CoNLL-U format files.
Neural network models for joint POS tagging and dependency parsing (CoNLL 2017-2018)
PhoNLP: A BERT-based multi-task learning model for part-of-speech tagging, named entity recognition and dependency parsing (NAACL 2021)
100 bài luyện tập xử lý ngôn ngữ tự nhiên
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/dependency-parsing。
- 该话题在 GitHub 上共有 151 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 1.2 万 Star,覆盖 4 种主要语言,数据更新于 2026-11-06。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。