Using deep neural networks, SyntaxNet and similar systems do take syntactic parsing to a new level. A neural net learns by analyzing vast amounts of data. It can learn to identify a photo of a cat, for instance, by analyzing millions of cat photos. In the case of SyntaxNet, it learns to understand sentences by analyzing millions of sentences. But these aren’t just any sentences. Humans have carefully labelled them, going through all the examples and carefully identifying the role that each word plays. After analyzing all these labeled sentences, the system can learn to identify similar characteristics in other sentences.
Though SyntaxNet is a tool for engineers and AI researchers, Google is also sharing a pre-built natural language processing service that it has already trained with the system. They call it, well, Parsey McParseface, and it’s trained for English, learning from a carefully labeled collection of old newswire stories. According to Google, Parsey McParseface is about 94 percent accurate in identifying how a word relates the rest of a sentence, a rate the company believes is close to the performance of a human (96 to 97 percent).
- More Here and git code here
Though SyntaxNet is a tool for engineers and AI researchers, Google is also sharing a pre-built natural language processing service that it has already trained with the system. They call it, well, Parsey McParseface, and it’s trained for English, learning from a carefully labeled collection of old newswire stories. According to Google, Parsey McParseface is about 94 percent accurate in identifying how a word relates the rest of a sentence, a rate the company believes is close to the performance of a human (96 to 97 percent).
- More Here and git code here
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