spaCy v2.0's Named Entity Recognition system features a sophisticated word embedding strategy using subword features and "Bloom" embeddings, a deep convolutional neural network with residual connections, and a novel transition-based approach to named entity parsing. The system is designed to give a good balance of efficiency, accuracy and adaptability. In this talk, I sketch out the components of the system, explaining the intuition behind the various choices. I also give a brief introduction to the named entity recognition problem, with an overview of what else Explosion AI is working on, and why. spaCy is a Python library for industrial-strength natural language processing. In November 2017 we released v2.0, which comes with 13 new convolutional neural network models for 7+ languages. SPACY ● Website: https://spacy.io ● Available models: https://spacy.io/models ● Named entity recognition: https://spacy.io/usage/linguistic-features#named-entities ● Neural network model architecture: https://spacy.io/api/#nn-model ● GitHub: https://github.com/explosion/spaCy ● Subreddit: https://www.reddit.com/r/spacynlp/ ● Twitter: https://twitter.com/spacy_io THIS VIDEO ● Slides: https://github.com/explosion/talks/blob/master/2017-11-02___Practical-and-Effective-Neural-NER.pdf ● Embed, encode, attend, predict: https://explosion.ai/blog/deep-learning-formula-nlp ● Prodigy annotation tool: https://prodi.gy FOLLOW US ● Explosion AI: https://twitter.com/explosion_ai ● Matthew Honnibal: https://twitter.com/honnibal ● Ines Montani: https://twitter.com/_inesmontani