Update intent parser docs and add to usage docs

examples/training/train_intent_parser.py

@@ -1,13 +1,13 @@
 #!/usr/bin/env python
 # coding: utf-8
-"""Using the parser to recognise your own semantics spaCy's parser component
-can be used to trained to predict any type of tree structure over your input
-text. You can also predict trees over whole documents or chat logs, with
-connections between the sentence-roots used to annotate discourse structure.
+"""Using the parser to recognise your own semantics
 
-In this example, we'll build a message parser for a common "chat intent":
-finding local businesses. Our message semantics will have the following types
-of relations: INTENT, PLACE, QUALITY, ATTRIBUTE, TIME, LOCATION. For example:
+spaCy's parser component can be used to trained to predict any type of tree
+structure over your input text. You can also predict trees over whole documents
+or chat logs, with connections between the sentence-roots used to annotate
+discourse structure. In this example, we'll build a message parser for a common
+"chat intent": finding local businesses. Our message semantics will have the
+following types of relations: ROOT, PLACE, QUALITY, ATTRIBUTE, TIME, LOCATION.
 
 "show me the best hotel in berlin"
 ('show', 'ROOT', 'show')

website/usage/_training/_tagger-parser.jade

@@ -95,6 +95,102 @@ p
     +item
         |  #[strong Test] the model to make sure the parser works as expected.
 
++h(3, "intent-parser") Training a parser for custom semantics
+
+p
+    |  spaCy's parser component can be used to trained to predict any type
+    |  of tree structure over your input text – including
+    |  #[strong semantic relations] that are not syntactic dependencies. This
+    |  can be useful to for #[strong conversational applications], which need to
+    | predict trees over whole documents or chat logs, with connections between
+    |  the sentence roots used to annotate discourse structure. For example, you
+    |  can train spaCy's parser to label intents and their targets, like
+    |  attributes, quality, time and locations. The result could look like this:
+
++codepen("991f245ef90debb78c8fc369294f75ad", 300)
+
++code.
+    doc = nlp(u"find a hotel with good wifi")
+    print([(t.text, t.dep_, t.head.text) for t in doc if t.dep_ != '-'])
+    # [('find', 'ROOT', 'find'), ('hotel', 'PLACE', 'find'),
+    #  ('good', 'QUALITY', 'wifi'), ('wifi', 'ATTRIBUTE', 'hotel')]
+
+p
+    |  The above tree attaches "wifi" to "hotel" and assigns the dependency
+    |  label #[code ATTRIBUTE]. This may not be a correct syntactic dependency –
+    |  but in this case, it expresses exactly what we need: the user is looking
+    |  for a hotel with the attribute "wifi" of the quality "good". This query
+    |  can then be processed by your application and used to trigger the
+    |  respective action – e.g. search the database for hotels with high ratings
+    |  for their wifi offerings.
+
++aside("Tip: merge phrases and entities")
+    |  To achieve even better accuracy, try merging multi-word tokens and
+    |  entities specific to your domain into one token before parsing your text.
+    |  You can do this by running the entity recognizer or
+    |  #[+a("/usage/linguistic-features#rule-based-matching") rule-based matcher]
+    |  to find relevant spans, and merging them using
+    |  #[+api("span#merge") #[code Span.merge]]. You could even add your own
+    |  custom #[+a("/usage/processing-pipelines#custom-components") pipeline component]
+    |  to do this automatically – just make sure to add it #[code before='parser'].
+
+p
+    |  The following example example shows a full implementation of a training
+    |  loop for a custom message parser for a common "chat intent": finding
+    |  local businesses. Our message semantics will have the following types
+    |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
+    |  #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].
+
++github("spacy", "examples/training/train_intent_parser.py")
+
++h(4) Step by step guide
+
++list("numbers")
+    +item
+        |  #[strong Create the training data] consisting of words, their heads
+        |  and their dependency labels in order. A token's head is the index
+        |  of the token it is attached to. The heads don't need to be
+        |  syntactically correct – they should express the
+        |  #[strong semantic relations] you want the parser to learn. For words
+        |  that shouldn't receive a label, you can choose an arbitrary
+        |  placeholder, for example #[code -].
+
+    +item
+        |  #[strong Load the model] you want to start with, or create an
+        |  #[strong empty model] using
+        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
+        |  language. If you're using a blank model, don't forget to add the
+        |  parser to the pipeline. If you're using an existing model,
+        |  make sure to disable all other pipeline components during training
+        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
+        |  This way, you'll only be training the parser.
+
+    +item
+        |  #[strong Add the dependency labels] to the parser using the
+        |  #[+api("dependencyparser#add_label") #[code add_label]] method.
+
+    +item
+        |  #[strong Shuffle and loop over] the examples and create a
+        |  #[code Doc] and #[code GoldParse] object for each example. Make sure
+        |  to pass in the #[code heads] and #[code deps] when you create the
+        |  #[code GoldParse].
+
+    +item
+        |  For each example, #[strong update the model]
+        |  by calling #[+api("language#update") #[code nlp.update]], which steps
+        |  through the words of the input. At each word, it makes a
+        |  #[strong prediction]. It then consults the annotations provided on the
+        |  #[code GoldParse] instance, to see whether it was
+        |  right. If it was wrong, it adjusts its weights so that the correct
+        |  action will score higher next time.
+
+    +item
+        |  #[strong Save] the trained model using
+        |  #[+api("language#to_disk") #[code nlp.to_disk]].
+
+    +item
+        |  #[strong Test] the model to make sure the parser works as expected.
+
 +h(3, "training-json") JSON format for training
 
 include ../../api/_annotation/_training

website/usage/examples.jade

@@ -122,6 +122,20 @@ include ../_includes/_mixins
 
     +github("spacy", "examples/training/train_tagger.py")
 
+    +h(3, "intent-parser") Training a custom parser for chat intent semantics
+
+    p
+        |  spaCy's parser component can be used to trained to predict any type
+        |  of tree structure over your input text. You can also predict trees
+        |  over whole documents or chat logs, with connections between the
+        |  sentence-roots used to annotate discourse structure. In this example,
+        |  we'll build a message parser for a common "chat intent": finding
+        |  local businesses. Our message semantics will have the following types
+        |  of relations: #[code ROOT], #[code PLACE], #[code QUALITY],
+        |  #[code ATTRIBUTE], #[code TIME] and #[code LOCATION].
+
+    +github("spacy", "examples/training/train_intent_parser.py")
+
     +h(3, "textcat") Training spaCy's text classifier
         +tag-new(2)