//- 💫 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("See Also")
    |  If you haven't read up on spaCy's #[+a("data-model") data model] yet,
    |  you should probably have a look. 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, "special-cases") Adding special case tokenization rules

p
    |  Most domains have at least some idiosyncrasies that require custom
    |  tokenization rules. 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')
    assert [w.text for w in nlp(u'gimme that')] == [u'gimme', u'that']
    nlp.tokenizer.add_special_case(u'gimme',
        [
            {
                ORTH: u'gim',
                LEMMA: u'give',
                POS: u'VERB'},
            {
                ORTH: u'me'}])
    assert [w.text for w in nlp(u'gimme that')] == [u'gim', u'me', u'that']
    # Pronoun lemma is returned as -PRON-
    # More details please see: https://spacy.io/docs/usage/troubleshooting#pron-lemma
    assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'-PRON-', 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.
    nlp.tokenizer.add_special_case(u'...gimme...?',
        [{
            ORTH: u'...gimme...?', LEMMA: u'give', TAG: u'VB'}])
    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, special_cases,
                              find_prefix, find_suffix, find_infixes):
        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(reversed(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. There
    |  are five 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.

    +item
        |  (Optional) A boolean function #[code token_match] matching strings
        |  that should never be split, overriding the previous rules.
        |  Useful for things like URLs or numbers.

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'''[\]\)"']''')
    infix_re = re.compile(r'''[-~]''')
    simple_url_re = re.compile(r'''^https?://''')
    def create_tokenizer(nlp):
        return Tokenizer(nlp.vocab,
                rules={},
                prefix_search=prefix_re.search,
                suffix_search=suffix_re.search,
                infix_finditer=infix_re.finditer,
                token_match=simple_url_re.match
                )

    nlp = spacy.load('en', create_make_doc=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.
    import spacy

    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(words)
            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.