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What’s new in Stanford NLP and Stanza

In this talk, I will discuss updates to Stanza, our Python natural language processing toolkit supporting 70 human languages. Compared to existing widely used toolkits, Stanza features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition.

I will talk about Stanza’s neural architectural design, its simple user interface, and its improved performance against existing toolkits over a range of datasets covering 70 languages. Our latest updates include NER support for a variety of new languages, a CNN-based sentiment model, a constituency parser, and a language detection model.

Lastly, I will talk about Stanza’s Python interface to the widely used Stanford CoreNLP Java library, which extends Stanza’s functionality to an even richer range of tasks.

I will close my talk by talking about our future plans for the Stanza library.

Combining Computer Vision and NLP for democratizing AI for Healthcare during pandemic

Machine Learning has been leveraged for a variety of medical tasks. However, much of the work has focused on feature extraction engineering...