#named entity recognition

Explore spaCy's robust NLP platform supporting over 70 languages using state-of-the-art neural networks. Access pretrained pipelines for essential tasks like tokenization, named entity recognition, and text classification. Leverage multi-task learning with BERT transformers, ensuring easy deployment and production-readiness. Enhance projects with custom models in frameworks like PyTorch or TensorFlow, and utilize powerful visualizers for syntax and NER. This open-source software, under the MIT license, offers high accuracy and extensibility for all your NLP needs.

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.

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.

Access a range of spaCy NLP models for various language processing tasks, available as `.whl` and `.tar.gz` files for efficient downloads. Installation commands ensure compatibility across spaCy versions. Models are classified by capabilities, training data types, and sizes, offering flexibility for different applications. Consult the documentation for detailed installation guidance and usage instructions. This repository is suitable for developers looking for customizable NLP solutions.

DeepKE provides a comprehensive toolkit for knowledge extraction, enabling entity, relation, and attribute extraction across scenarios like cnSchema, low-resource, and multimodal. It includes useful resources like documentation and demos to aid integration into diverse environments, enhancing knowledge graph construction through models like NER and relation extraction.