💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy

Pipeline component for spaCy (and other spaCy-wrapped parsers such as spacy-stanza and spacy-udpipe) that adds CoNLL-U properties to a Doc and its sentences and tokens. Can also be used as a command-line tool.

TensorFlow Models for the Stanford Question Answering Dataset

Chinese implementation of the Python official interface for Stanford CoreNLP Java server application to parse, tokenize, part-of-speech tag, etc. Chinese texts.

All lecture notes, slides and assignments from CS224n: Natural Language Processing with Deep Learning class by Stanford

A simple text based AI to execute commands using NLP

Stanford University cs224n Assignments solutions

Stanford CS224n: Natural Language Processing with Deep Learning

A rule-based, English language, passive voice converter. Requires spaCy.

Converting training data set built by lang-uk community to the format supported by Stanza NLP library

Named Entity Recognition for standard entities and sentiment analysis.

End-to-end Knowledge Extraction engine. It extracts knowledge from free text and shows the knowledge in Neo4j. It extracts entities and the relationship between entities, even different expressions of the same entity is in different sentences of the text.

Short overview on the must popular models for Named Entity Recognition

In this Project I have used Standford Tools to extract the named entity and relation between those entity

Stanford NLP with Deep Learning course (WI 19)

Assignment solutions

Bachelor of Engineering degree project. Analysing medical claims using engines such a s StanfordNLP and MetaMap.

Information extraction using pre-trained models

Project where we analyzed school curriculums by converting school textbooks to knowledge graphs.

Dockerized Python application for analyzing data stored in RDBMS using Jupyter Notebook