//- 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 idiosyncracies that require custom | tokenization rules. Here's how to add a special case rule to an existing | #[+api("tokenizer") #[code Tokenizer]] instance: +code. 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'] 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: "give", TAG: "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, 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. 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_make_doc) 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(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.