#dependency parsing

Explore Chinese NLP fundamentals through detailed explanations of key techniques including segmentation, POS tagging, and named entity recognition. Authored by HanLP creator Han He, this project translates complex models into digestible concepts, offering professional development opportunities with insightful personal notes. Access additional resources like mind maps and related project links for a comprehensive learning journey.

The spacy-stanza package combines Stanza's, formerly StanfordNLP, models with spaCy, allowing integration of high-accuracy models for tasks like tokenization, POS tagging, and lemmatization across 68 languages. It supports advanced NLP tasks including named entity recognition using Stanza's sophisticated algorithms. Ideal for developers looking to leverage the strengths of both SpaCy and Stanza, it provides customizable options within SpaCy's pipeline and supports user-defined components. Compatible with SpaCy v3.0 and above for optimal performance.

Underthesea is an open-source toolkit offering accessible NLP tools for Vietnamese text, including word segmentation, POS tagging, sentiment analysis, and more. Its integration with prompt-based models and comprehensive datasets supports robust text processing in Python. The project invites contributions and collaboration from its community.

SceneGraphParser is a Python-based toolkit that transforms sentences into scene graphs using dependency parsing. Unlike the Stanford Scene Graph Parser, it offers an intuitive interface and simple configuration. It identifies nouns and their relational connections, supporting visual-semantic embeddings and bridging vision with language. The tool uses spaCy as its backend, facilitating integration into Python projects without complex data handling. Participation in identifying failure cases is welcomed to advance development.