* Add spec.jade

docs/redesign/spec.jade

@@ -0,0 +1,123 @@
+extends ./outline.jade
+
+mixin columns(...names)
+  tr
+    each name in names
+      th= name
+
+
+mixin row(...cells)
+  tr
+    each cell in cells
+      td= cell
+
+
+block body_block
+  article(class="page docs-page")
+    p.
+      This document describes the target annotations spaCy is trained to predict.
+      This is currently a work in progress. Please ask questions on the issue tracker,
+      so that the answers can be integrated here to improve the documentation.
+
+    h2 Tokenization
+
+    p Tokenization standards are based on the OntoNotes 5 corpus.
+
+    p.
+      The tokenizer differs from most by including tokens for significant
+      whitespace. Any sequence of whitespace characters beyond a single space
+      (' ') is included as a token. For instance:
+
+    pre.language-python
+      code
+        | from spacy.en import English
+        | nlp = English(parse=False)
+        | tokens = nlp('Some\nspaces  and\ttab characters')
+        | print([t.orth_ for t in tokens])
+        
+    p Which produces:
+    
+    pre.language-python
+      code
+        | ['Some', '\n', 'spaces', ' ', 'and', '\t', 'tab', 'characters']
+
+    p.
+      The whitespace tokens are useful for much the same reason punctuation is
+      – it's often an important delimiter in the text.  By preserving
+      it in the token output, we are able to maintain a simple alignment
+      between the tokens and the original string, and we ensure that no
+      information is lost during processing.
+
+    h3 Sentence boundary detection
+
+    p.
+      Sentence boundaries are calculated from the syntactic parse tree, so
+      features such as punctuation and capitalisation play an important but
+      non-decisive role in determining the sentence boundaries.  Usually this
+      means that the sentence boundaries will at least coincide with clause
+      boundaries, even given poorly punctuated text.
+
+    h3 Part-of-speech Tagging
+
+    p.
+      The part-of-speech tagger uses the OntoNotes 5 version of the Penn Treebank
+      tag set.  We also map the tags to the simpler Google Universal POS Tag set.
+
+      Details here: https://github.com/honnibal/spaCy/blob/master/spacy/en/pos.pyx#L124
+
+    h3 Lemmatization
+
+    p.
+      A "lemma" is the uninflected form of a word. In English, this means:
+
+    ul
+      li Adjectives: The form like "happy", not "happier" or "happiest"
+      li Adverbs: The form like "badly", not "worse" or "worst"
+      li Nouns: The form like "dog", not "dogs"; like "child", not "children"
+      li Verbs: The form like "write", not "writes", "writing", "wrote" or "written" 
+
+    p.
+      The lemmatization data is taken from WordNet. However, we also add a
+      special case for pronouns: all pronouns are lemmatized to the special
+      token -PRON-.
+
+
+    h3 Syntactic Dependency Parsing
+
+    p.
+      The parser is trained on data produced by the ClearNLP converter. Details
+      of the annotation scheme can be found here:  http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf
+
+    h3 Named Entity Recognition
+
+    table
+      thead
+        +columns("Entity Type", "Description")
+      
+      tbody
+        +row("PERSON", "People, including fictional.")
+        +row("NORP", "Nationalities or religious or political groups.")
+        +row("FACILITY", "Buildings, airports, highways, bridges, etc.")
+        +row("ORG", "Companies, agencies, institutions, etc.")
+        +row("GPE", "Countries, cities, states.")
+        +row("LOC", "Non-GPE locations, mountain ranges, bodies of water.")
+        +row("PRODUCT", "Vehicles, weapons, foods, etc. (Not services")
+        +row("EVENT", "Named hurricanes, battles, wars, sports events, etc.")
+        +row("WORK_OF_ART", "Titles of books, songs, etc.")
+        +row("LAW", "Named documents made into laws")
+        +row("LANGUAGE", "Any named language")
+
+    p The following values are also annotated in a style similar to names:
+
+    table
+      thead
+        +columns("Entity Type", "Description")
+      
+      tbody
+        +row("DATE", "Absolute or relative dates or periods")
+        +row("TIME", "Times smaller than a day")
+        +row("PERCENT", 'Percentage (including “%”)')
+        +row("MONEY", "Monetary values, including unit")
+        +row("QUANTITY", "Measurements, as of weight or distance")
+        +row("ORDINAL", 'first", "second"')
+        +row("CARDINAL", "Numerals that do not fall under another type")