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Add "Adding languages" workflow (closes #562)

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Ines Montani 2016-12-18 23:51:09 +01:00
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website/docs/usage/_data.json
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@ -16,7 +16,8 @@
"Word vectors": "word-vectors-similarities",
"Deep learning": "deep-learning",
"Custom tokenization": "customizing-tokenizer",
"Training": "training"
"Training": "training",
"Adding languages": "adding-languages"
},
"Examples": {
"Tutorials": "tutorials",
@ -88,6 +89,11 @@
"next": "dependency-parse"
},
"adding-languages": {
"title": "Adding languages",
"next": "training"
},
"showcase": {
"title": "Showcase",

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website/docs/usage/adding-languages.jade Normal file
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//- 💫 DOCS > USAGE > ADDING LANGUAGES
include ../../_includes/_mixins
p
| Adding full support for a language touches many different parts of the
| spaCy library. This guide explains how to fit everything together, and
| points you to the specific workflows for each component. Obviously,
| there are lots of ways you can organise your code when you implement
| your own #[+api("language") #[code Language]] class. This guide will
| focus on how it's done within spaCy. For full language support, we'll
| need to:
+list("numbers")
+item
| Create a #[strong #[code Language] subclass] and
| #[a(href="#language-subclass") implement it].
+item
| Define custom #[strong language data], like a
| #[a(href="#stop-words") stop list], #[a(href="#tag-map") tag map]
| and #[a(href="#tokenizer-exceptions") tokenizer exceptions].
+item
| #[strong Build the vocabulary] including
| #[a(href="#word-probabilities") word probabilities],
| #[a(href="#brown-clusters") Brown clusters] and
| #[a(href="#word-vectors") word vectors].
p
| Once you have the tokenizer and vocabulary, you can
| #[+a("/docs/usage/training") train the tagger, parser and entity recognizer].
| For some languages, you may also want to develop a solution for
| lemmatization and morphological analysis.
+h(2, "language-subclass") Creating a #[code Language] subclass
p
| Language-specific code and resources should be organised into a
| subpackage of spaCy, named according to the language's
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code].
| For instance, code and resources specific to Spanish are placed into a
| folder #[code spacy/es], which can be imported as #[code spacy.es].
p
| To get started, you can use our
| #[+src(gh("spacy-dev-resources", "templates/new_language")) templates]
| for the most important files. Here's what the class template looks like:
+code("__init__.py (excerpt)").
# Import language-specific data
from .language_data import *
class Xxxxx(Language):
lang = 'xx' # ISO code
class Defaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: 'xx'
# override defaults
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
tag_map = TAG_MAP
stop_words = STOP_WORDS
p Additionally, the new #[code Language] class needs to be registered in #[+src(gh("spaCy", "spacy/__init__.py")) spacy/__init__.py] using the #[code set_lang_class()] function, so that you can use #[code spacy.load()].
+code("spacy/__init__.py").
from . import en
from . import xx
set_lang_class(en.English.lang, en.English)
set_lang_class(xx.Xxxxx.lang, xx.Xxxxx)
p You'll also need to list the new package in #[+src(gh("spaCy", "spacy/setup.py")) setup.py]:
+code("spacy/setup.py").
PACKAGES = [
'spacy',
'spacy.tokens',
'spacy.en',
'spacy.xx',
# ...
]
+h(2, "language-data") Adding language data
p
| Every language is full of exceptions and special cases, especially
| amongst the most common words. Some of these exceptions are shared
| between multiple languages, while others are entirely idiosyncratic.
| spaCy makes it easy to deal with these exceptions on a case-by-case
| basis, by defining simple rules and exceptions. The exceptions data is
| defined in Python the
| #[+src(gh("spacy-dev-resources", "templates/new_language")) language data],
| so that Python functions can be used to help you generalise and combine
| the data as you require.
+h(3, "stop-words") Stop words
p
| A #[+a("https://en.wikipedia.org/wiki/Stop_words") "stop list"] is a
| classic trick from the early days of information retrieval when search
| was largely about keyword presence and absence. It is still sometimes
| useful today to filter out common words from a bag-of-words model.
+aside("What does spaCy consider a stop word?")
| There's no particularly principal logic behind what words should be
| added to the stop list. Make a list that you think might be useful
| to people and is likely to be unsurprising. As a rule of thumb, words
| that are very rare are unlikely to be useful stop words.
p
| To improve readability, #[code STOP_WORDS] are separated by spaces and
| newlines, and added as a multiline string:
+code("Example").
STOP_WORDS = set("""
a about above across after afterwards again against all almost alone along
already also although always am among amongst amount an and another any anyhow
anyone anything anyway anywhere are around as at
back be became because become becomes becoming been before beforehand behind
being below beside besides between beyond both bottom but by
""").split())
+h(3, "tag-map") Tag map
p
| Most treebanks define a custom part-of-speech tag scheme, striking a
| balance between level of detail and ease of prediction. While it's
| useful to have custom tagging schemes, it's also useful to have a common
| scheme, to which the more specific tags can be related. The tagger can
| learn a tag scheme with any arbitrary symbols. However, you need to
| define how those symbols map down to the
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies tag set].
| This is done by providing a tag map.
p
| The keys of the tag map should be #[strong strings in your tag set]. The
| values should be a dictionary. The dictionary must have an entry POS
| whose value is one of the
| #[+a("http://universaldependencies.org/u/pos/all.html") Universal Dependencies]
| tags. Optionally, you can also include morphological features or other
| token attributes in the tag map as well. This allows you to do simple
| #[+a("/docs/usage/pos-tagging#rule-based-morphology") rule-based morphological analysis].
+code("Example").
TAG_MAP = {
"NNS": {POS: NOUN, "Number": "plur"},
"VBG": {POS: VERB, "VerbForm": "part", "Tense": "pres", "Aspect": "prog"},
"DT": {POS: DET}
}
+h(3, "tokenizer-exceptions") Tokenizer exceptions
p
| spaCy's #[+a("/docs/usage/customizing-tokenizer#how-tokenizer-works") tokenization algorithm]
| lets you deal with whitespace-delimited chunks separately. This makes it
| easy to define special-case rules, without worrying about how they
| interact with the rest of the tokenizer. Whenever the key string is
| matched, the special-case rule is applied, giving the defined sequence of
| tokens. You can also attach attributes to the subtokens, covered by your
| special case, such as the subtokens #[code LEMMA] or #[code TAG].
p
| Tokenizer exceptions can be added in the following format:
+code("language_data.py").
TOKENIZER_EXCEPTIONS = {
"don't": [
{ORTH: "do", LEMMA: "do"},
{ORTH: "n't", LEMMA: "not", TAG: "RB"}
]
}
p
| Some exceptions, like certain abbreviations, will always be mapped to a
| single token containing only an #[code ORTH] property. To make your data
| less verbose, you can use the helper function #[code strings_to_exc()]
| with a simple array of strings:
+code("Example").
from ..language_data import update_exc, strings_to_exc
ORTH_ONLY = ["a.", "b.", "c."]
converted = strings_to_exc(ORTH_ONLY)
# {"a.": [{ORTH: "a."}], "b.": [{ORTH: "b."}], "c.": [{ORTH: "c."}]}
update_exc(TOKENIZER_EXCEPTIONS, converted)
p
| Unambiguous abbreviations, like month names or locations in English,
| should be added to #[code TOKENIZER_EXCEPTIONS] with a lemma assigned,
| for example #[code {ORTH: "Jan.", LEMMA: "January"}].
+h(3, "custom-tokenizer-exceptions") Custom tokenizer exceptions
p
| For language-specific tokenizer exceptions, you can use the
| #[code update_exc()] function to update the existing exceptions with a
| custom dictionary. This is especially useful for exceptions that follow
| a consistent pattern. Instead of adding each exception manually, you can
| write a simple function that returns a dictionary of exceptions.
p
| For example, here's how exceptions for time formats like "1a.m." and
| "1am" are generated in the English
| #[+src(gh("spaCy", "spacy/en/language_data.py")) language_data.py]:
+code("language_data.py").
from ..language_data import update_exc
def get_time_exc(hours):
exc = {}
for hour in hours:
exc["%da.m." % hour] = [{ORTH: hour}, {ORTH: "a.m."}]
exc["%dp.m." % hour] = [{ORTH: hour}, {ORTH: "p.m."}]
exc["%dam" % hour] = [{ORTH: hour}, {ORTH: "am", LEMMA: "a.m."}]
exc["%dpm" % hour] = [{ORTH: hour}, {ORTH: "pm", LEMMA: "p.m."}]
return exc
TOKENIZER_EXCEPTIONS = dict(language_data.TOKENIZER_EXCEPTIONS)
hours = 12
update_exc(TOKENIZER_EXCEPTIONS, get_time_exc(range(1, hours + 1)))
+h(3, "utils") Shared utils
p
| The #[code spacy.language_data] package provides constants and functions
| that can be imported and used across languages.
+aside("About spaCy's custom pronoun lemma")
| Unlike verbs and common nouns, there's no clear base form of a personal
| pronoun. Should the lemma of "me" be "I", or should we normalize person
| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
| novel symbol, #[code.u-nowrap -PRON-], which is used as the lemma for
| all personal pronouns.
+table(["Name", "Description"])
+row
+cell #[code PRON_LEMMA]
+cell
| Special value for pronoun lemmas (#[code "-PRON-"]).
+row
+cell #[code ENT_ID]
+cell
| Special value for entity IDs (#[code "ent_id"])
+row
+cell #[code update_exc(exc, additions)]
+cell
| Update an existing dictionary of exceptions #[code exc] with a
| dictionary of #[code additions].
+row
+cell #[code strings_to_exc(orths)]
+cell
| Convert an array of strings to a dictionary of exceptions of the
| format #[code {"string": [{ORTH: "string"}]}].
+row
+cell #[code expand_exc(excs, search, replace)]
+cell
| Search for a string #[code search] in a dictionary of exceptions
| #[code excs] and if found, copy the entry and replace
| #[code search] with #[code replace] in both the key and
| #[code ORTH] value. Useful to provide exceptions containing
| different versions of special unicode characters, like
| #[code '] and #[code ’].
p
| If you've written a custom function that seems like it might be useful
| for several languages, consider adding it to
| #[+src(gh("spaCy", "spacy/language_data/util.py")) language_data/util.py]
| instead of the individual language module.
+h(3, "shared-data") Shared language data
p
| Because languages can vary in quite arbitrary ways, spaCy avoids
| organising the language data into an explicit inheritance hierarchy.
| Instead, reuseable functions and data are collected as atomic pieces in
| the #[code spacy.language_data] package.
+aside-code("Example").
from ..language_data import update_exc, strings_to_exc
from ..language_data import EMOTICONS
# Add custom emoticons
EMOTICONS = EMOTICONS + ["8===D", ":~)"]
# Add emoticons to tokenizer exceptions
update_exc(TOKENIZER_EXCEPTIONS, strings_to_exc(EMOTICONS))
+table(["Name", "Description", "Source"])
+row
+cell #[code EMOTICONS]
+cell
| Common unicode emoticons without whitespace.
+cell
+src(gh("spaCy", "spacy/language_data/emoticons.py")) emoticons.py
+row
+cell #[code TOKENIZER_PREFIXES]
+cell
| Regular expressions to match left-attaching tokens and
| punctuation, e.g. #[code $], #[code (], #[code "]
+cell
+src(gh("spaCy", "spacy/language_data/punctuation.py")) punctuation.py
+row
+cell #[code TOKENIZER_SUFFIXES]
+cell
| Regular expressions to match right-attaching tokens and
| punctuation, e.g. #[code %], #[code )], #[code "]
+cell
+src(gh("spaCy", "spacy/language_data/punctuation.py")) punctuation.py
+row
+cell #[code TOKENIZER_INFIXES]
+cell
| Regular expressions to match token separators, e.g. #[code -]
+cell
+src(gh("spaCy", "spacy/language_data/punctuation.py")) punctuation.py
+row
+cell #[code TAG_MAP]
+cell
| A tag map keyed by the universal part-of-speech tags to
| themselves with no morphological features.
+cell
+src(gh("spaCy", "spacy/language_data/tag_map.py")) tag_map.py
+row
+cell #[code ENTITY_RULES]
+cell
| Patterns for named entities commonly missed by the statistical
| entity recognizer, for use in the rule matcher.
+cell
+src(gh("spaCy", "spacy/language_data/entity_rules.py")) entity_rules.py
+row
+cell #[code FALSE_POSITIVES]
+cell
| Patterns for phrases commonly mistaken for named entities by the
| statistical entity recognizer, to use in the rule matcher.
+cell
+src(gh("spaCy", "spacy/language_data/entity_rules.py")) entity_rules.py
p
| Individual languages can extend and override any of these expressions.
| Often, when a new language is added, you'll find a pattern or symbol
| that's missing. Even if this pattern or symbol isn't common in other
| languages, it might be best to add it to the base expressions, unless it
| has some conflicting interpretation. For instance, we don't expect to
| see guillemot quotation symbols (#[code »] and #[code «]) in
| English text. But if we do see them, we'd probably prefer the tokenizer
| to split it off.
+h(2, "vocabulary") Building the vocabulary
p
| spaCy expects that common words will be cached in a
| #[+api("vocab") #[code Vocab]] instance. The vocabulary caches lexical
| features, and makes it easy to use information from unlabelled text
| samples in your models. Specifically, you'll usually want to collect
| word frequencies, and train two types of distributional similarity model:
| Brown clusters, and word vectors. The Brown clusters are used as features
| by linear models, while the word vectors are useful for lexical
| similarity models and deep learning.
p
| Once you've collected the word frequencies, Brown clusters and word
| vectors files, you can use the
| #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
| script from our
| #[+a(gh("spacy-developer-resources")) developer resources] to create a
| spaCy data directory:
+code(false, "bash").
python training/init.py xx your_data_directory/ my_data/word_freqs.txt my_data/clusters.txt my_data/word_vectors.bz2
+aside-code("your_data_directory", "yaml").
├── vocab/
| ├── lexemes.bin # via nlp.vocab.dump(path)
| ├── strings.json # via nlp.vocab.strings.dump(file_)
| └── oov_prob # optional
├── pos/ # optional
| ├── model # via nlp.tagger.model.dump(path)
| └── config.json # via Langage.train
├── deps/ # optional
| ├── model # via nlp.parser.model.dump(path)
| └── config.json # via Langage.train
└── ner/ # optional
├── model # via nlp.entity.model.dump(path)
└── config.json # via Langage.train
p
| This creates a spaCy data directory with a vocabulary model, ready to be
| loaded. By default, the
| #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
| script expects to be able to find your language class using
| #[code spacy.util.get_lang_class(lang_id)]. You can edit the script to
| help it find your language class if necessary.
+h(3, "word-frequencies") Word frequencies
p
| The #[code init.py] script expects a tab-separated word frequencies file
| with three columns: the number of times the word occurred in your language
| sample, the number of distinct documents the word occurred in, and the
| word itself. You should make sure you use the spaCy tokenizer for your
| language to segment the text for your word frequencies. This will ensure
| that the frequencies refer to the same segmentation standards you'll be
| using at run-time. For instance, spaCy's English tokenizer segments "can't"
| into two tokens. If we segmented the text by whitespace to produce the
| frequency counts, we'll have incorrect frequency counts for the tokens
| "ca" and "n't".
+h(3, "brown-clusters") Training the Brown clusters
p
| spaCy's tagger, parser and entity recognizer are designed to use
| distributional similarity features provided by the
| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
| You should train a model with between 500 and 1000 clusters. A minimum
| frequency threshold of 10 usually works well.
+h(3, "word-vectors") Training the word vectors
p
| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
| algorithms let you train useful word similarity models from unlabelled
| text. This is a key part of using
| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
| labelled data. The vectors are also useful by themselves – they power
| the #[code .similarity()] methods in spaCy. For best results, you should
| pre-process the text with spaCy before training the Word2vec model. This
| ensures your tokenization will match.
p
| You can use our
| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
| which pre-processes the text with your language-specific tokenizer and
| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].