Software projects have a high failure rate, and the failure rate for projects using machine learning technologies such as natural language processing is even higher.
Matthew Honnibal shares “one weird trick” that can give your NLP project a better chance of success: avoid a waterfall methodology where data definition, corpus construction, modeling, and deployment are performed as separate phases of work.
Matthew Honnibal is the creator and lead developer of spaCy, one of the most popular libraries for natural language processing. He’s been publishing research on NLP since 2005, with a focus on syntactic parsing and other structured prediction problems. He left academia to start working on spaCy in 2014.
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