spaCy/website/usage/_linguistic-features/_tokenization.jade

//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > TOKENIZATION

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.

include ../_spacy-101/_tokenization

+h(4, "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")) #[code 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.").

+graphic("/assets/img/language_data.svg")
    include ../../assets/img/language_data.svg

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

+h(3, "special-cases") Adding special case tokenization rules

p
    |  Most domains have at least some idiosyncrasies 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")) #[code 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']
    # Pronoun lemma is returned as -PRON-!
    assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'-PRON-', u'that']

p
    |  For details on spaCy's custom pronoun lemma #[code -PRON-],
    |  #[+a("/usage/#pron-lemma") see here].
    |  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(3, "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(3, "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 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
        |  An optional 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 custom_tokenizer(nlp):
        return Tokenizer(nlp.vocab, 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')
    nlp.tokenizer = custom_tokenizer(nlp)

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(3, "custom-tokenizer") Hooking an arbitrary tokenizer into the pipeline

p
    |  The tokenizer is the first component of the processing pipeline and the
    |  only one that can't be replaced by writing to #[code nlp.pipeline]. This
    |  is because it has a different signature from all the other components:
    |  it takes a text and returns a #[code Doc], whereas all other components
    |  expect to already receive a tokenized #[code Doc].

+graphic("/assets/img/pipeline.svg")
    include ../../assets/img/pipeline.svg

p
    |  To overwrite the existing tokenizer, you need to replace
    |  #[code nlp.tokenizer] with a custom function that takes a text, and
    |  returns a #[code Doc].

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

+table(["Argument", "Type", "Description"])
    +row
        +cell #[code text]
        +cell unicode
        +cell The raw text to tokenize.

    +row("foot")
        +cell returns
        +cell #[code Doc]
        +cell The tokenized document.

+infobox("Important note: using a custom tokenizer")
    .o-block
        |  In spaCy v1.x, you had to add a custom tokenizer by passing it to the
        |  #[code make_doc] keyword argument, or by passing a tokenizer "factory"
        |  to #[code create_make_doc]. This was unnecessarily complicated. Since
        |  spaCy v2.0, you can simply write to #[code nlp.tokenizer]. If your
        |  tokenizer needs the vocab, you can write a function and use
        |  #[code nlp.vocab].

    +code-new.
        nlp.tokenizer = my_tokenizer
        nlp.tokenizer = my_tokenizer_factory(nlp.vocab)
    +code-old.
        nlp = spacy.load('en', make_doc=my_tokenizer)
        nlp = spacy.load('en', create_make_doc=my_tokenizer_factory)

+h(3, "custom-tokenizer-example") Example: A custom whitespace tokenizer

p
    |  To construct the tokenizer, we usually want attributes of the #[code nlp]
    |  pipeline. Specifically, we want the tokenizer to hold a reference to the
    |  vocabulary object. Let's say we have the following class as
    |  our tokenizer:

+code.
    from spacy.tokens import Doc

    class WhitespaceTokenizer(object):
        def __init__(self, vocab):
            self.vocab = 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 have it until we get back the loaded #[code nlp] object. The
    |  simplest solution is to build the tokenizer in two steps. This also means
    |  that you can reuse the "tokenizer factory" and initialise it with
    |  different instances of #[code Vocab].

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

+h(3, "own-annotations") Bringing your own annotations

p
    |  spaCy generally assumes by default that your data is raw text. However,
    |  sometimes your data is partially annotated, e.g. with pre-existing
    |  tokenization, part-of-speech tags, etc. The most common situation is
    |  that you have pre-defined tokenization. If you have a list of strings,
    |  you can create a #[code Doc] object directly. Optionally, you can also
    |  specify a list of boolean values, indicating whether each word has a
    |  subsequent space.

+code.
    doc = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])

p
    |  If provided, the spaces list must be the same length as the words list.
    |  The spaces list affects the #[code doc.text], #[code span.text],
    |  #[code token.idx], #[code span.start_char] and #[code span.end_char]
    |  attributes. If you don't provide a #[code spaces] sequence, spaCy will
    |  assume that all words are whitespace delimited.

+code.
    good_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'], spaces=[False, True, False, False])
    bad_spaces = Doc(nlp.vocab, words=[u'Hello', u',', u'world', u'!'])
    assert bad_spaces.text == u'Hello , world !'
    assert good_spaces.text == u'Hello, world!'

p
    |  Once you have a #[+api("doc") #[code Doc]] object, you can write to its
    |  attributes to set the part-of-speech tags, syntactic dependencies, named
    |  entities and other attributes. For details, see the respective usage
    |  pages.