Merge branch 'develop' of https://github.com/explosion/spaCy into develop

spacy/cli/package.py

@@ -101,7 +101,7 @@ def generate_meta():
 def generate_pipeline():
     prints("If set to 'True', the default pipeline is used. If set to 'False', "
            "the pipeline will be disabled. Components should be specified as a "
-           "comma-separated list of component names, e.g. tensorizer, tagger, "
+           "comma-separated list of component names, e.g. tagger, "
            "parser, ner. For more information, see the docs on processing pipelines.",
            title="Enter your model's pipeline components")
     pipeline = util.get_raw_input("Pipeline components", True)

spacy/lemmatizer.py

@@ -7,8 +7,8 @@ from .symbols import VerbForm_inf, VerbForm_none, Number_sing, Degree_pos
 
 class Lemmatizer(object):
     @classmethod
-    def load(cls, path, index=None, exc=None, rules=None):
+    def load(cls, path, index=None, exc=None, rules=None, lookup=None):
-        return cls(index or {}, exc or {}, rules or {})
+        return cls(index or {}, exc or {}, rules or {}, lookup or {})
 
     def __init__(self, index=None, exceptions=None, rules=None, lookup=None):
         self.index = index if index is not None else {}
@@ -26,10 +26,10 @@ class Lemmatizer(object):
         elif univ_pos in (PUNCT, 'PUNCT', 'punct'):
             univ_pos = 'punct'
         else:
-            return set([string.lower()])
+            return list(set([string.lower()]))
         # See Issue #435 for example of where this logic is requied.
         if self.is_base_form(univ_pos, morphology):
-            return set([string.lower()])
+            return list(set([string.lower()]))
         lemmas = lemmatize(string, self.index.get(univ_pos, {}),
                            self.exc.get(univ_pos, {}),
                            self.rules.get(univ_pos, []))
@@ -108,4 +108,4 @@ def lemmatize(string, index, exceptions, rules):
         forms.extend(oov_forms)
     if not forms:
         forms.append(string)
-    return set(forms)
+    return list(set(forms))

spacy/morphology.pyx

@@ -172,7 +172,7 @@ cdef class Morphology:
         cdef unicode py_string = self.strings[orth]
         if self.lemmatizer is None:
             return self.strings.add(py_string.lower())
-        cdef set lemma_strings
+        cdef list lemma_strings
         cdef unicode lemma_string
         lemma_strings = self.lemmatizer(py_string, univ_pos, morphology)
         lemma_string = sorted(lemma_strings)[0]

spacy/tests/spans/test_span.py

@@ -117,3 +117,9 @@ def test_span_to_array(doc):
     assert arr[0, 0] == span[0].orth
     assert arr[0, 1] == len(span[0])
 
+
+@pytest.mark.xfail
+def test_span_as_doc(doc):
+    span = doc[4:10]
+    span_doc = span.as_doc()
+    assert span.text == span_doc.text

website/_includes/_mixins.jade

@@ -181,7 +181,7 @@ mixin codepen(slug, height, default_tab)
     alt_file - [string] alternative file path used in footer and link button
     height   - [integer] height of code preview in px
 
-mixin github(repo, file, alt_file, height)
+mixin github(repo, file, alt_file, height, language)
     - var branch = ALPHA ? "develop" : "master"
     - var height = height || 250
 

website/api/_annotation/_training.jade

@@ -0,0 +1,46 @@
+//- 💫 DOCS > API > ANNOTATION > TRAINING
+
+p
+    |  spaCy takes training data in JSON format. The built-in
+    |  #[+api("cli#convert") #[code convert]] command helps you convert the
+    |  #[code .conllu] format used by the
+    |  #[+a("https://github.com/UniversalDependencies") Universal Dependencies corpora]
+    |  to spaCy's training format.
+
++aside("Annotating entities")
+    |  Named entities are provided in the #[+a("/api/annotation#biluo") BILUO]
+    |  notation. Tokens outside an entity are set to #[code "O"] and tokens
+    |  that are part of an entity are set to the entity label, prefixed by the
+    |  BILUO marker. For example #[code "B-ORG"] describes the first token of
+    |  a multi-token #[code ORG] entity and #[code "U-PERSON"] a single
+    |  token representing a #[code PERSON] entity
+
++code("Example structure").
+    [{
+        "id": int,                      # ID of the document within the corpus
+        "paragraphs": [{                # list of paragraphs in the corpus
+            "raw": string,              # raw text of the paragraph
+            "sentences": [{             # list of sentences in the paragraph
+                "tokens": [{            # list of tokens in the sentence
+                    "id": int,          # index of the token in the document
+                    "dep": string,      # dependency label
+                    "head": int,        # offset of token head relative to token index
+                    "tag": string,      # part-of-speech tag
+                    "orth": string,     # verbatim text of the token
+                    "ner": string       # BILUO label, e.g. "O" or "B-ORG"
+                }],
+                "brackets": [{          # phrase structure (NOT USED by current models)
+                    "first": int,       # index of first token
+                    "last": int,        # index of last token
+                    "label": string     # phrase label
+                }]
+            }]
+        }]
+    }]
+
+p
+    |  Here's an example of dependencies, part-of-speech tags and names
+    |  entities, taken from the English Wall Street Journal portion of the Penn
+    |  Treebank:
+
++github("spacy", "examples/training/training-data.json", false, false, "json")

website/api/_data.json

@@ -154,13 +154,16 @@
 
     "tokenizer": {
         "title": "Tokenizer",
+        "teaser": "Segment text into words, punctuations marks etc.",
         "tag": "class",
         "source": "spacy/tokenizer.pyx"
     },
 
     "lemmatizer": {
         "title": "Lemmatizer",
-        "tag": "class"
+        "teaser": "Assign the base forms of words.",
+        "tag": "class",
+        "source": "spacy/lemmatizer.py"
     },
 
     "tagger": {

website/api/annotation.jade

@@ -101,31 +101,4 @@ p This document describes the target annotations spaCy is trained to predict.
 +section("training")
     +h(2, "json-input") JSON input format for training
 
-    +under-construction
+    include _annotation/_training
-
-    p spaCy takes training data in the following format:
-
-    +code("Example structure").
-        doc: {
-            id: string,
-            paragraphs: [{
-                raw: string,
-                sents: [int],
-                tokens: [{
-                    start: int,
-                    tag: string,
-                    head: int,
-                    dep: string
-                }],
-                ner: [{
-                    start: int,
-                    end: int,
-                    label: string
-                }],
-                brackets: [{
-                    start: int,
-                    end: int,
-                    label: string
-                }]
-            }]
-        }

website/api/lemmatizer.jade

@@ -2,4 +2,159 @@
 
 include ../_includes/_mixins
 
-+under-construction
+p
+    |  The #[code Lemmatizer] supports simple part-of-speech-sensitive suffix
+    |  rules and lookup tables.
+
++h(2, "init") Lemmatizer.__init__
+    +tag method
+
+p Create a #[code Lemmatizer].
+
++aside-code("Example").
+    from spacy.lemmatizer import Lemmatizer
+    lemmatizer = Lemmatizer()
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code index]
+        +cell dict / #[code None]
+        +cell Inventory of lemmas in the language.
+
+    +row
+        +cell #[code exceptions]
+        +cell dict / #[code None]
+        +cell Mapping of string forms to lemmas that bypass the #[code rules].
+
+    +row
+        +cell #[code rules]
+        +cell dict / #[code None]
+        +cell List of suffix rewrite rules.
+
+    +row
+        +cell #[code lookup]
+        +cell dict / #[code None]
+        +cell Lookup table mapping string to their lemmas.
+
+    +row("foot")
+        +cell returns
+        +cell #[code Lemmatizer]
+        +cell The newly created object.
+
++h(2, "call") Lemmatizer.__call__
+    +tag method
+
+p Lemmatize a string.
+
++aside-code("Example").
+    from spacy.lemmatizer import Lemmatizer
+    from spacy.lang.en import LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES
+    lemmatizer = Lemmatizer(LEMMA_INDEX, LEMMA_EXC, LEMMA_RULES)
+    lemmas = lemmatizer(u'ducks', u'NOUN')
+    assert lemmas == [u'duck']
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code string]
+        +cell unicode
+        +cell The string to lemmatize, e.g. the token text.
+
+    +row
+        +cell #[code univ_pos]
+        +cell unicode / int
+        +cell The token's universal part-of-speech tag.
+
+    +row
+        +cell #[code morphology]
+        +cell dict / #[code None]
+        +cell
+            |  Morphological features following the
+            |  #[+a("http://universaldependencies.org/") Universal Dependencies]
+            |  scheme.
+
+    +row("foot")
+        +cell returns
+        +cell list
+        +cell The available lemmas for the string.
+
++h(2, "lookup") Lemmatizer.lookup
+    +tag method
+    +tag-new(2)
+
+p
+    |  Look up a lemma in the lookup table, if available. If no lemma is found,
+    |  the original string is returned. Languages can provide a
+    |  #[+a("/usage/adding-languages#lemmatizer") lookup table] via the
+    |  #[code lemma_lookup] variable, set on the individual #[code Language]
+    |  class.
+
++aside-code("Example").
+    lookup = {u'going': u'go'}
+    lemmatizer = Lemmatizer(lookup=lookup)
+    assert lemmatizer.lookup(u'going') == u'go'
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code string]
+        +cell unicode
+        +cell The string to look up.
+
+    +row("foot")
+        +cell returns
+        +cell unicode
+        +cell The lemma if the string was found, otherwise the original string.
+
++h(2, "is_base_form") Lemmatizer.is_base_form
+    +tag method
+
+p
+    |  Check whether we're dealing with an uninflected paradigm, so we can
+    |  avoid lemmatization entirely.
+
++aside-code("Example").
+    pos = 'verb'
+    morph = {'VerbForm': 'inf'}
+    is_base_form = lemmatizer.is_base_form(pos, morph)
+    assert is_base_form == True
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code univ_pos]
+        +cell unicode / int
+        +cell The token's universal part-of-speech tag.
+
+    +row
+        +cell #[code morphology]
+        +cell dict
+        +cell The token's morphological features.
+
+    +row("foot")
+        +cell returns
+        +cell bool
+        +cell
+            |  Whether the token's part-of-speech tag and morphological features
+            |  describe a base form.
+
++h(2, "attributes") Attributes
+
++table(["Name", "Type", "Description"])
+    +row
+        +cell #[code index]
+        +cell dict / #[code None]
+        +cell Inventory of lemmas in the language.
+
+    +row
+        +cell #[code exc]
+        +cell dict / #[code None]
+        +cell Mapping of string forms to lemmas that bypass the #[code rules].
+
+    +row
+        +cell #[code rules]
+        +cell dict / #[code None]
+        +cell List of suffix rewrite rules.
+
+    +row
+        +cell #[code lookup_table]
+            +tag-new(2)
+        +cell dict / #[code None]
+        +cell The lemma lookup table, if available.

website/usage/_training/_tagger-parser.jade

@@ -1,3 +1,7 @@
 //- 💫 DOCS > USAGE > TRAINING > TAGGER & PARSER
 
 +under-construction
+
++h(3, "training-json") JSON format for training
+
+include ../../api/_annotation/_training