doc: Fix minor mistakes

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mpuels 2017-12-13 11:37:24 +01:00 committed by GitHub
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+h(3, "intent-parser") Training a parser for custom semantics +h(3, "intent-parser") Training a parser for custom semantics
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| spaCy's parser component can be used to trained to predict any type | spaCy's parser component can be used to be trained to predict any type
| of tree structure over your input text  including | of tree structure over your input text  including
| #[strong semantic relations] that are not syntactic dependencies. This | #[strong semantic relations] that are not syntactic dependencies. This
| can be useful to for #[strong conversational applications], which need to | can be useful to for #[strong conversational applications], which need to
| predict trees over whole documents or chat logs, with connections between | predict trees over whole documents or chat logs, with connections between
| the sentence roots used to annotate discourse structure. For example, you | the sentence roots used to annotate discourse structure. For example, you
| can train spaCy's parser to label intents and their targets, like | can train spaCy's parser to label intents and their targets, like
| attributes, quality, time and locations. The result could look like this: | attributes, quality, time and locations. The result could look like this:
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| to do this automatically just make sure to add it #[code before='parser']. | to do this automatically just make sure to add it #[code before='parser'].
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| The following example example shows a full implementation of a training | The following example shows a full implementation of a training
| loop for a custom message parser for a common "chat intent": finding | loop for a custom message parser for a common "chat intent": finding
| local businesses. Our message semantics will have the following types | local businesses. Our message semantics will have the following types
| of relations: #[code ROOT], #[code PLACE], #[code QUALITY], | of relations: #[code ROOT], #[code PLACE], #[code QUALITY],