//- 💫 DOCS > USAGE > TOKENIZER

include ../../_includes/_mixins

p
    |  Tokenization is the task of splitting a text into meaningful segments,
    |  called #[em tokens].  The input to the tokenizer is a unicode text, and
    |  the output is a #[+api("doc") #[code Doc]] object. To construct a
    |  #[code Doc] object, you need a #[+api("vocab") #[code Vocab]] instance,
    |  a sequence of #[code word] strings, and optionally a sequence of
    |  #[code spaces] booleans, which allow you to maintain alignment of the
    |  tokens into the original string.

+aside("spaCy's data model")
    |  The main point to keep in mind is that spaCy's #[code Doc] doesn't
    |  copy or refer to the original string. The string is reconstructed from
    |  the tokens when required.

+h(2, "101") Tokenizer 101

include _spacy-101/_tokenization


+h(3, "101-data") Tokenizer data

p
    |  #[strong Global] and #[strong language-specific] tokenizer data is
    |  supplied via the language data in #[+src(gh("spaCy", "spacy/lang")) spacy/lang].
    |  The tokenizer exceptions define special cases like "don't" in English,
    |  which needs to be split into two tokens: #[code {ORTH: "do"}] and
    |  #[code {ORTH: "n't", LEMMA: "not"}]. The prefixes, suffixes and infixes
    |  mosty define punctuation rules – for example, when to split off periods
    |  (at the end of a sentence), and when to leave token containing periods
    |  intact (abbreviations like "U.S.").

+image
    include ../../assets/img/docs/language_data.svg
    .u-text-right
        +button("/assets/img/docs/language_data.svg", false, "secondary").u-text-tag View large graphic

+infobox
    |  For more details on the language-specific data, see the
    |  usage workflow on #[+a("/docs/usage/adding-languages") adding languages].

+h(2, "special-cases") Adding special case tokenization rules

p
    |  Most domains have at least some idiosyncracies that require custom
    |  tokenization rules. This could be very certain expressions, or
    |  abbreviations only used in this specific field.

+aside("Language data vs. custom tokenization")
    |  Tokenization rules that are specific to one language, but can be
    |  #[strong generalised across that language] should ideally live in the
    |  language data in #[+src(gh("spaCy", "spacy/lang")) spacy/lang] – we
    |  always appreciate pull requests! Anything that's specific to a domain or
    |  text type – like financial trading abbreviations, or Bavarian youth slang
    |  – should be added as a special case rule to your tokenizer instance. If
    |  you're dealing with a lot of customisations, it might make sense to create
    |  an entirely custom subclass.

p
    |  Here's how to add a special case rule to an existing
    |  #[+api("tokenizer") #[code Tokenizer]] instance:

+code.
    import spacy
    from spacy.symbols import ORTH, LEMMA, POS

    nlp = spacy.load('en')
    doc = nlp(u'gimme that') # phrase to tokenize
    assert [w.text for w in doc] == [u'gimme', u'that'] # current tokenization

    # add special case rule
    special_case = [{ORTH: u'gim', LEMMA: u'give', POS: u'VERB'}, {ORTH: u'me'}]
    nlp.tokenizer.add_special_case(u'gimme', special_case)
    assert [w.text for w in nlp(u'gimme that')] == [u'gim', u'me', u'that']
    assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'me', u'that']

p
    |  The special case doesn't have to match an entire whitespace-delimited
    |  substring. The tokenizer will incrementally split off punctuation, and
    |  keep looking up the remaining substring:

+code.
    assert 'gimme' not in [w.text for w in nlp(u'gimme!')]
    assert 'gimme' not in [w.text for w in nlp(u'("...gimme...?")')]

p
    |  The special case rules have precedence over the punctuation splitting:

+code.
    special_case = [{ORTH: u'...gimme...?', LEMMA: u'give', TAG: u'VB'}]
    nlp.tokenizer.add_special_case(u'...gimme...?', special_case)
    assert len(nlp(u'...gimme...?')) == 1

p
    |  Because the special-case rules allow you to set arbitrary token
    |  attributes, such as the part-of-speech, lemma, etc, they make a good
    |  mechanism for arbitrary fix-up rules. Having this logic live in the
    |  tokenizer isn't very satisfying from a design perspective, however, so
    |  the API may eventually be exposed on the
    |  #[+api("language") #[code Language]] class itself.


+h(2, "how-tokenizer-works") How spaCy's tokenizer works

p
    |  spaCy introduces a novel tokenization algorithm, that gives a better
    |  balance between performance, ease of definition, and ease of alignment
    |  into the original string.

p
    |  After consuming a prefix or infix, we consult the special cases again.
    |  We want the special cases to handle things like "don't" in English, and
    |  we want the same rule to work for "(don't)!". We do this by splitting
    |  off the open bracket, then the exclamation, then the close bracket, and
    |  finally matching the special-case. Here's an implementation of the
    |  algorithm in Python, optimized for readability rather than performance:

+code.
    def tokenizer_pseudo_code(text, find_prefix, find_suffix,
                              find_infixes, special_cases):
        tokens = []
        for substring in text.split(' '):
            suffixes = []
            while substring:
                if substring in special_cases:
                    tokens.extend(special_cases[substring])
                    substring = ''
                elif find_prefix(substring) is not None:
                    split = find_prefix(substring)
                    tokens.append(substring[:split])
                    substring = substring[split:]
                elif find_suffix(substring) is not None:
                    split = find_suffix(substring)
                    suffixes.append(substring[split:])
                    substring = substring[:split]
                elif find_infixes(substring):
                    infixes = find_infixes(substring)
                    offset = 0
                    for match in infixes:
                        tokens.append(substring[i : match.start()])
                        tokens.append(substring[match.start() : match.end()])
                        offset = match.end()
                    substring = substring[offset:]
                else:
                    tokens.append(substring)
                    substring = ''
            tokens.extend(suffixes)
            return tokens

p
    |  The algorithm can be summarized as follows:

+list("numbers")
    +item Iterate over space-separated substrings
    +item
        |  Check whether we have an explicitly defined rule for this substring.
        |  If we do, use it.
    +item Otherwise, try to consume a prefix.
    +item
        |  If we consumed a prefix, go back to the beginning of the loop, so
        |  that special-cases always get priority.
    +item If we didn't consume a prefix, try to consume a suffix.
    +item
        |  If we can't consume a prefix or suffix, look for "infixes" — stuff
        |  like hyphens etc.
    +item Once we can't consume any more of the string, handle it as a single token.

+h(2, "native-tokenizers") Customizing spaCy's Tokenizer class

p
    |  Let's imagine you wanted to create a tokenizer for a new language or
    |  specific domain. There are four things you would need to define:

+list("numbers")
    +item
        |  A dictionary of #[strong special cases]. This handles things like
        |  contractions, units of measurement, emoticons, certain
        |  abbreviations, etc.

    +item
        |  A function #[code prefix_search], to handle
        |  #[strong preceding punctuation], such as open quotes, open brackets,
        |  etc

    +item
        |  A function #[code suffix_search], to handle
        |  #[strong succeeding punctuation], such as commas, periods, close
        |  quotes, etc.

    +item
        |  A function #[code infixes_finditer], to handle non-whitespace
        |  separators, such as hyphens etc.

p
    |  You shouldn't usually need to create a #[code Tokenizer] subclass.
    |  Standard usage is to use #[code re.compile()] to build a regular
    |  expression object, and pass its #[code .search()] and
    |  #[code .finditer()] methods:

+code.
    import re
    from spacy.tokenizer import Tokenizer

    prefix_re = re.compile(r'''[\[\("']''')
    suffix_re = re.compile(r'''[\]\)"']''')

    def create_tokenizer(nlp):
        return Tokenizer(nlp.vocab, prefix_search=prefix_re.search,
                                    suffix_search=suffix_re.search)

    nlp = spacy.load('en', tokenizer=create_tokenizer)

p
    |  If you need to subclass the tokenizer instead, the relevant methods to
    |  specialize are #[code find_prefix], #[code find_suffix] and
    |  #[code find_infix].

+h(2, "custom-tokenizer") Hooking an arbitrary tokenizer into the pipeline

p
    |  You can pass a custom tokenizer using the #[code make_doc] keyword, when
    |  you're creating the pipeline:

+code.
    nlp = spacy.load('en', make_doc=my_tokenizer)

p
    |  However, this approach often leaves us with a chicken-and-egg problem.
    |  To construct the tokenizer, we usually want attributes of the #[code nlp]
    |  pipeline. Specifically, we want the tokenizer to hold a reference to the
    |  pipeline's vocabulary object. Let's say we have the following class as
    |  our tokenizer:


+code.
    import spacy
    from spacy.tokens import Doc

    class WhitespaceTokenizer(object):
        def __init__(self, nlp):
            self.vocab = nlp.vocab

        def __call__(self, text):
            words = text.split(' ')
            # All tokens 'own' a subsequent space character in this tokenizer
            spaces = [True] * len(word)
            return Doc(self.vocab, words=words, spaces=spaces)

p
    |  As you can see, we need a #[code vocab] instance to construct this — but
    |  we won't get the #[code vocab] instance until we get back the #[code nlp]
    |  object from #[code spacy.load()]. The simplest solution is to build the
    |  object in two steps:

+code.
    nlp = spacy.load('en')
    nlp.make_doc = WhitespaceTokenizer(nlp)

p
    |  You can instead pass the class to the #[code create_make_doc] keyword,
    |  which is invoked as callback once the #[code nlp] object is ready:

+code.
    nlp = spacy.load('en', create_make_doc=WhitespaceTokenizer)

p
    |  Finally, you can of course create your own subclasses, and create a bound
    |  #[code make_doc] method. The disadvantage of this approach is that spaCy
    |  uses inheritance to give each language-specific pipeline its own class.
    |  If you're working with multiple languages, a naive solution will
    |  therefore require one custom class per language you're working with.
    |  This might be at least annoying. You may be able to do something more
    |  generic by doing some clever magic with metaclasses or mixins, if that's
    |  the sort of thing you're into.