Explosion

FEBRUARY 18, 2015

spaCy is a new library for text processing in Python and Cython. I wrote it because I think small companies are terrible at natural language processing (NLP). Or rather: small companies are using terrible NLP technology. This post shows the original launch announcement for spaCy , which came with some usage examples and benchmarks. The benchmarks are quite out of date, but I’m pleased to say usage has changed relatively little.

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Clustering Natural Language Processing Machine Learning Machine Learning 52

Hacker News

FEBRUARY 10, 2015

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Analytics 140

Analytics Analytics 140

Explosion

FEBRUARY 18, 2015

Computers don't understand text. This is unfortunate, because that's what the web almost entirely consists of. We want to recommend people text based on other text they liked. We want to shorten text to display it on a mobile screen. We want to aggregate it, link it, filter it, categorise it, generate it and correct it. spaCy provides a library of utility functions that help programmers build such products.

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Explosion

FEBRUARY 18, 2015

This post was pushed out in a hurry, immediately after spaCy was released. It explains some of how spaCy is designed and implemented, and provides some quick notes explaining which algorithms were used. The post pre-dates spaCy's named entity recogniser, but it provides some detail about the tokenisation algorithm, general design, and efficiency concerns.

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Algorithm 40

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Explosion

FEBRUARY 18, 2015

The following are some hasty preliminary notes on how spaCy works. The short story is, there are no new killer algorithms. The way that the tokenizer works is novel and a bit neat, and the parser has a new feature set, but otherwise the key algorithms are well known in the recent literature. This post was pushed out in a hurry, immediately after spaCy was released.

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Algorithm Python Clustering 40