Lithuanian language support (#3895)

* initial LT lang support

* Added more stopwords. Started setting up some basic test environment (not complete)

* Initial morph rules for LT lang

* Closes #1 Adds tokenizer exceptions for Lithuanian

* Closes #5 Punctuation rules. Closes #6 Lexical Attributes

* test: add native examples to basic tests

* feat: add tag map for lt lang

* fix: remove undefined tag attribute 'Definite'

* feat: add lemmatizer for lt lang

* refactor: add new instances to lt lang morph rules; use tags from tag map

* refactor: add morph rules to lt lang defaults

* refactor: only keep nouns, verbs, adverbs and adjectives in lt lang lemmatizer lookup

* refactor: add capitalized words to lt lang lemmatizer

* refactor: add more num words to lt lang lex attrs

* refactor: update lt lang stop word set

* refactor: add new instances to lt lang tokenizer exceptions

* refactor: remove comments form lt lang init file

* refactor: use function instead of lambda in lt lex lang getter

* refactor: remove conversion to dict in lt init when dict is already provided

* chore: rename lt 'test_basic' to 'test_text'

* feat: add more lt text tests

* feat: add lemmatizer tests

* refactor: remove unused imports, add newline to end of file

* chore: add contributor agreement

* chore: change 'en' to 'lt' in lt example description

* fix: add missing encoding info

* style: add newline to end of file

* refactor: use python2 compatible syntax

* style: reformat code using black
This commit is contained in:
Rokas Ramanauskas 2019-07-08 11:25:22 +03:00 committed by Ines Montani
parent 4f1dae1c6b
commit 61ce126d4c
13 changed files with 245058 additions and 483 deletions

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.github/contributors/rokasramas.md vendored Normal file
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# spaCy contributor agreement
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## Contributor Details
| Field | Entry |
|------------------------------- | ----------------------- |
| Name | Rokas Ramanauskas |
| Company name (if applicable) | TokenMill |
| Title or role (if applicable) | Software Engineer |
| Date | 2019-07-02 |
| GitHub username | rokasramas |
| Website (optional) | http://www.tokenmill.lt |

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@ -1,15 +1,37 @@
# coding: utf8 # coding: utf8
from __future__ import unicode_literals from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .tag_map import TAG_MAP
from .lemmatizer import LOOKUP
from .morph_rules import MORPH_RULES
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ..norm_exceptions import BASE_NORMS
from ...language import Language from ...language import Language
from ...attrs import LANG from ...attrs import LANG, NORM
from ...util import update_exc, add_lookups
def _return_lt(_):
return "lt"
class LithuanianDefaults(Language.Defaults): class LithuanianDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters) lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: "lt" lex_attr_getters[LANG] = _return_lt
lex_attr_getters[NORM] = add_lookups(
Language.Defaults.lex_attr_getters[NORM], BASE_NORMS
)
lex_attr_getters.update(LEX_ATTRS)
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
stop_words = STOP_WORDS stop_words = STOP_WORDS
tag_map = TAG_MAP
morph_rules = MORPH_RULES
lemma_lookup = LOOKUP
class Lithuanian(Language): class Lithuanian(Language):

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# coding: utf8
from __future__ import unicode_literals
"""
Example sentences to test spaCy and its language models.
>>> from spacy.lang.lt.examples import sentences
>>> docs = nlp.pipe(sentences)
"""
sentences = [
"Jaunikis pirmąją vestuvinę naktį iškeitė į areštinės gultą",
"Bepiločiai automobiliai išnaikins vairavimo mokyklas, autoservisus ir eismo nelaimes",
"Vilniuje galvojama uždrausti naudoti skėčius",
"Londonas yra didelis miestas Jungtinėje Karalystėje",
"Kur tu?",
"Kas yra Prancūzijos prezidentas?",
"Kokia yra Jungtinių Amerikos Valstijų sostinė?",
"Kada gimė Dalia Grybauskaitė?",
]

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@ -0,0 +1,268 @@
# coding: utf8
from __future__ import unicode_literals
from ...symbols import ORTH
_exc = {}
for orth in [
"G.",
"J. E.",
"J. Em.",
"J.E.",
"J.Em.",
"K.",
"N.",
"V.",
"Vt.",
"a.",
"a.k.",
"a.s.",
"adv.",
"akad.",
"aklg.",
"akt.",
"al.",
"ang.",
"angl.",
"aps.",
"apskr.",
"apyg.",
"arbat.",
"asist.",
"asm.",
"asm.k.",
"asmv.",
"atk.",
"atsak.",
"atsisk.",
"atsisk.sąsk.",
"atv.",
"aut.",
"avd.",
"b.k.",
"baud.",
"biol.",
"bkl.",
"bot.",
"bt.",
"buv.",
"ch.",
"chem.",
"corp.",
"d.",
"dab.",
"dail.",
"dek.",
"deš.",
"dir.",
"dirig.",
"doc.",
"dol.",
"dr.",
"drp.",
"dvit.",
"dėst.",
"dš.",
"dž.",
"e.b.",
"e.bankas",
"e.p.",
"e.parašas",
"e.paštas",
"e.v.",
"e.valdžia",
"egz.",
"eil.",
"ekon.",
"el.",
"el.bankas",
"el.p.",
"el.parašas",
"el.paštas",
"el.valdžia",
"etc.",
"ež.",
"fak.",
"faks.",
"feat.",
"filol.",
"filos.",
"g.",
"gen.",
"geol.",
"gerb.",
"gim.",
"gr.",
"gv.",
"gyd.",
"gyv.",
"habil.",
"inc.",
"insp.",
"inž.",
"ir pan.",
"ir t. t.",
"isp.",
"istor.",
"it.",
"just.",
"k.",
"k. a.",
"k.a.",
"kab.",
"kand.",
"kart.",
"kat.",
"ketv.",
"kh.",
"kl.",
"kln.",
"km.",
"kn.",
"koresp.",
"kpt.",
"kr.",
"kt.",
"kub.",
"kun.",
"kv.",
"kyš.",
"l. e. p.",
"l.e.p.",
"lenk.",
"liet.",
"lot.",
"lt.",
"ltd.",
"ltn.",
"m.",
"m.e..",
"m.m.",
"mat.",
"med.",
"mgnt.",
"mgr.",
"min.",
"mjr.",
"ml.",
"mln.",
"mlrd.",
"mob.",
"mok.",
"moksl.",
"mokyt.",
"mot.",
"mr.",
"mst.",
"mstl.",
"mėn.",
"nkt.",
"no.",
"nr.",
"ntk.",
"nuotr.",
"op.",
"org.",
"orig.",
"p.",
"p.d.",
"p.m.e.",
"p.s.",
"pab.",
"pan.",
"past.",
"pav.",
"pavad.",
"per.",
"perd.",
"pirm.",
"pl.",
"plg.",
"plk.",
"pr.",
"pr.Kr.",
"pranc.",
"proc.",
"prof.",
"prom.",
"prot.",
"psl.",
"pss.",
"pvz.",
"pšt.",
"r.",
"raj.",
"red.",
"rez.",
"rež.",
"rus.",
"rš.",
"s.",
"sav.",
"saviv.",
"sek.",
"sekr.",
"sen.",
"sh.",
"sk.",
"skg.",
"skv.",
"skyr.",
"sp.",
"spec.",
"sr.",
"st.",
"str.",
"stud.",
"sąs.",
"t.",
"t. p.",
"t. y.",
"t.p.",
"t.t.",
"t.y.",
"techn.",
"tel.",
"teol.",
"th.",
"tir.",
"trit.",
"trln.",
"tšk.",
"tūks.",
"tūkst.",
"up.",
"upl.",
"v.s.",
"vad.",
"val.",
"valg.",
"ved.",
"vert.",
"vet.",
"vid.",
"virš.",
"vlsč.",
"vnt.",
"vok.",
"vs.",
"vtv.",
"vv.",
"vyr.",
"vyresn.",
"zool.",
"Įn",
"įl.",
"š.m.",
"šnek.",
"šv.",
"švč.",
"ž.ū.",
"žin.",
"žml.",
"žr.",
]:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = _exc

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@ -124,6 +124,16 @@ def ja_tokenizer():
return get_lang_class("ja").Defaults.create_tokenizer() return get_lang_class("ja").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def lt_tokenizer():
return get_lang_class("lt").Defaults.create_tokenizer()
@pytest.fixture(scope="session")
def lt_lemmatizer():
return get_lang_class("lt").Defaults.create_lemmatizer()
@pytest.fixture(scope="session") @pytest.fixture(scope="session")
def nb_tokenizer(): def nb_tokenizer():
return get_lang_class("nb").Defaults.create_tokenizer() return get_lang_class("nb").Defaults.create_tokenizer()

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@ -0,0 +1,15 @@
# coding: utf-8
from __future__ import unicode_literals
import pytest
@pytest.mark.parametrize("tokens,lemmas", [
(["Galime", "vadinti", "gerovės", "valstybe", ",", "turime", "išvystytą", "socialinę", "apsaugą", ",",
"sveikatos", "apsaugą", "ir", "prieinamą", "švietimą", "."],
["galėti", "vadintas", "gerovė", "valstybė", ",", "turėti", "išvystytas", "socialinis",
"apsauga", ",", "sveikata", "apsauga", "ir", "prieinamas", "švietimas", "."]),
(["taip", ",", "uoliai", "tyrinėjau", "ir", "pasirinkau", "geriausią", "variantą", "."],
["taip", ",", "uolus", "tyrinėti", "ir", "pasirinkti", "geras", "variantas", "."])])
def test_lt_lemmatizer(lt_lemmatizer, tokens, lemmas):
assert lemmas == [lt_lemmatizer.lookup(token) for token in tokens]

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# coding: utf-8
from __future__ import unicode_literals
import pytest
def test_lt_tokenizer_handles_long_text(lt_tokenizer):
text = """Tokios sausros kriterijus atitinka pirmadienį atlikti skaičiavimai, palyginus faktinį ir žemiausią
vidutinį daugiametį vandens lygį. Nustatyta, kad 48 šalies vandens matavimo stočių 28-iose stotyse vandens lygis
yra žemesnis arba lygus žemiausiam vidutiniam daugiamečiam šiltojo laikotarpio vandens lygiui."""
tokens = lt_tokenizer(text.replace("\n", ""))
assert len(tokens) == 42
@pytest.mark.parametrize('text,length', [
("177R Parodų rūmaiOzo g. nuo vasario 18 d. bus skelbiamas interneto tinklalapyje.", 15),
("ISM universiteto doc. dr. Ieva Augutytė-Kvedaravičienė pastebi, kad tyrimais nustatyti elgesio pokyčiai.", 16)])
def test_lt_tokenizer_handles_punct_abbrev(lt_tokenizer, text, length):
tokens = lt_tokenizer(text)
assert len(tokens) == length
@pytest.mark.parametrize("text", ["km.", "pvz.", "biol."])
def test_lt_tokenizer_abbrev_exceptions(lt_tokenizer, text):
tokens = lt_tokenizer(text)
assert len(tokens) == 1
@pytest.mark.parametrize("text,match", [
("10", True),
("1", True),
("10,000", True),
("10,00", True),
("999.0", True),
("vienas", True),
("du", True),
("milijardas", True),
("šuo", False),
(",", False),
("1/2", True)])
def test_lt_lex_attrs_like_number(lt_tokenizer, text, match):
tokens = lt_tokenizer(text)
assert len(tokens) == 1
assert tokens[0].like_num == match