Added alpha support for Tagalog language (#3062)

I have added alpha support for the Tagalog language from the Philippines. It is the basis for the country's national language Filipino. I have heavily based the format to the EN and ES languages.

I have provided several words in the lemmatizer lookup table, added stop words from a source, translated numeric words to its Tagalog counterpart, added some tokenizer exceptions, and kept the tag map the same as the English language.

While the alpha language passed the preliminary testing that you provided, I think it needs more data to be useful for most cases.

* Added alpha support for Tagalog language

* Edited contributor template

* Included SCA; Reverted templates

* Fixed SCA template

* Fixed changes in SCA template
This commit is contained in:
Brixjohn 2018-12-18 20:08:38 +08:00 committed by Matthew Honnibal
parent c9a89bba50
commit 52f3c95004
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# spaCy contributor agreement
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## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Brixter John Lumabi |
| Company name (if applicable) | Stratpoint |
| Title or role (if applicable) | Software Developer |
| Date | 18 December 2018 |
| GitHub username | Brixjohn |
| Website (optional) | |

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# coding: utf8
from __future__ import unicode_literals
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
# uncomment if files are available
# from .norm_exceptions import NORM_EXCEPTIONS
from .tag_map import TAG_MAP
# from .morph_rules import MORPH_RULES
# uncomment if lookup-based lemmatizer is available
from .lemmatizer import LOOKUP
# from ...lemmatizerlookup import Lemmatizer
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ..norm_exceptions import BASE_NORMS
from ...language import Language
from ...attrs import LANG, NORM
from ...util import update_exc, add_lookups
def _return_tl(_):
return 'tl'
# Create a Language subclass
# Documentation: https://spacy.io/docs/usage/adding-languages
# This file should be placed in spacy/lang/xx (ISO code of language).
# Before submitting a pull request, make sure the remove all comments from the
# language data files, and run at least the basic tokenizer tests. Simply add the
# language ID to the list of languages in spacy/tests/conftest.py to include it
# in the basic tokenizer sanity tests. You can optionally add a fixture for the
# language's tokenizer and add more specific tests. For more info, see the
# tests documentation: https://github.com/explosion/spaCy/tree/master/spacy/tests
class TagalogDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = _return_tl # ISO code
# add more norm exception dictionaries here
lex_attr_getters[NORM] = add_lookups(Language.Defaults.lex_attr_getters[NORM], BASE_NORMS)
# overwrite functions for lexical attributes
lex_attr_getters.update(LEX_ATTRS)
# add custom tokenizer exceptions to base exceptions
tokenizer_exceptions = update_exc(BASE_EXCEPTIONS, TOKENIZER_EXCEPTIONS)
# add stop words
stop_words = STOP_WORDS
# if available: add tag map
# tag_map = dict(TAG_MAP)
# if available: add morph rules
# morph_rules = dict(MORPH_RULES)
# if available: add lookup lemmatizer
# @classmethod
# def create_lemmatizer(cls, nlp=None):
# return Lemmatizer(LOOKUP)
class Tagalog(Language):
lang = 'tl' # ISO code
Defaults = TagalogDefaults # set Defaults to custom language defaults
# set default export this allows the language class to be lazy-loaded
__all__ = ['Tagalog']

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# coding: utf8
from __future__ import unicode_literals
# Adding a lemmatizer lookup table
# Documentation: https://spacy.io/docs/usage/adding-languages#lemmatizer
# Entries should be added in the following format:
LOOKUP = {
"kaugnayan": "ugnay",
"sangkatauhan": "tao",
"kanayunan": "nayon",
"pandaigdigan": "daigdig",
"kasaysayan": "saysay",
"kabayanihan": "bayani",
"karuwagan": "duwag"
}

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# coding: utf8
from __future__ import unicode_literals
# import the symbols for the attrs you want to overwrite
from ...attrs import LIKE_NUM
# Overwriting functions for lexical attributes
# Documentation: https://localhost:1234/docs/usage/adding-languages#lex-attrs
# Most of these functions, like is_lower or like_url should be language-
# independent. Others, like like_num (which includes both digits and number
# words), requires customisation.
# Example: check if token resembles a number
_num_words = ['sero', 'isa', 'dalawa', 'tatlo', 'apat', 'lima', 'anim', 'pito',
'walo', 'siyam', 'sampu', 'labing-isa', 'labindalawa', 'labintatlo', 'labing-apat',
'labinlima', 'labing-anim', 'labimpito', 'labing-walo', 'labinsiyam', 'dalawampu',
'tatlumpu', 'apatnapu', 'limampu', 'animnapu', 'pitumpu', 'walumpu', 'siyamnapu',
'daan', 'libo', 'milyon', 'bilyon', 'trilyon', 'quadrilyon',
'gajilyon', 'bazilyon']
def like_num(text):
text = text.replace(',', '').replace('.', '')
if text.isdigit():
return True
if text.count('/') == 1:
num, denom = text.split('/')
if num.isdigit() and denom.isdigit():
return True
if text in _num_words:
return True
return False
# Create dictionary of functions to overwrite. The default lex_attr_getters are
# updated with this one, so only the functions defined here are overwritten.
LEX_ATTRS = {
LIKE_NUM: like_num
}

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# encoding: utf8
from __future__ import unicode_literals
# Add stop words
# Documentation: https://spacy.io/docs/usage/adding-languages#stop-words
# To improve readability, words should be ordered alphabetically and separated
# by spaces and newlines. When adding stop words from an online source, always
# include the link in a comment. Make sure to proofread and double-check the
# words lists available online are often known to contain mistakes.
# data from https://github.com/stopwords-iso/stopwords-tl/blob/master/stopwords-tl.txt
STOP_WORDS = set("""
akin
aking
ako
alin
am
amin
aming
ang
ano
anumang
apat
at
atin
ating
ay
bababa
bago
bakit
bawat
bilang
dahil
dalawa
dapat
din
dito
doon
gagawin
gayunman
ginagawa
ginawa
ginawang
gumawa
gusto
habang
hanggang
hindi
huwag
iba
ibaba
ibabaw
ibig
ikaw
ilagay
ilalim
ilan
inyong
isa
isang
itaas
ito
iyo
iyon
iyong
ka
kahit
kailangan
kailanman
kami
kanila
kanilang
kanino
kanya
kanyang
kapag
kapwa
karamihan
katiyakan
katulad
kaya
kaysa
ko
kong
kulang
kumuha
kung
laban
lahat
lamang
likod
lima
maaari
maaaring
maging
mahusay
makita
marami
marapat
masyado
may
mayroon
mga
minsan
mismo
mula
muli
na
nabanggit
naging
nagkaroon
nais
nakita
namin
napaka
narito
nasaan
ng
ngayon
ni
nila
nilang
nito
niya
niyang
noon
o
pa
paano
pababa
paggawa
pagitan
pagkakaroon
pagkatapos
palabas
pamamagitan
panahon
pangalawa
para
paraan
pareho
pataas
pero
pumunta
pumupunta
sa
saan
sabi
sabihin
sarili
sila
sino
siya
tatlo
tayo
tulad
tungkol
una
walang
""".split())

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# coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, ADV, NOUN, ADP, PRON, SCONJ, PROPN, DET, SYM, INTJ
from ...symbols import PUNCT, NUM, AUX, X, CONJ, ADJ, VERB, PART, SPACE, CCONJ
# Add a tag map
# Documentation: https://spacy.io/docs/usage/adding-languages#tag-map
# Universal Dependencies: http://universaldependencies.org/u/pos/all.html
# The keys of the tag map should be strings in your tag set. The dictionary must
# have an entry POS whose value is one of the Universal Dependencies tags.
# Optionally, you can also include morphological features or other attributes.
TAG_MAP = {
"ADV": {POS: ADV},
"NOUN": {POS: NOUN},
"ADP": {POS: ADP},
"PRON": {POS: PRON},
"SCONJ": {POS: SCONJ},
"PROPN": {POS: PROPN},
"DET": {POS: DET},
"SYM": {POS: SYM},
"INTJ": {POS: INTJ},
"PUNCT": {POS: PUNCT},
"NUM": {POS: NUM},
"AUX": {POS: AUX},
"X": {POS: X},
"CONJ": {POS: CONJ},
"CCONJ": {POS: CCONJ},
"ADJ": {POS: ADJ},
"VERB": {POS: VERB},
"PART": {POS: PART},
"SP": {POS: SPACE}
}

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# coding: utf8
from __future__ import unicode_literals
# import symbols if you need to use more, add them here
from ...symbols import ORTH, LEMMA, TAG, NORM, ADP, DET
# Add tokenizer exceptions
# Documentation: https://spacy.io/docs/usage/adding-languages#tokenizer-exceptions
# Feel free to use custom logic to generate repetitive exceptions more efficiently.
# If an exception is split into more than one token, the ORTH values combined always
# need to match the original string.
# Exceptions should be added in the following format:
_exc = {
"tayo'y": [
{ORTH: "tayo", LEMMA: "tayo"},
{ORTH: "'y", LEMMA: "ay"}],
"isa'y": [
{ORTH: "isa", LEMMA: "isa"},
{ORTH: "'y", LEMMA: "ay"}],
"baya'y": [
{ORTH: "baya", LEMMA: "bayan"},
{ORTH: "'y", LEMMA: "ay"}],
"sa'yo": [
{ORTH: "sa", LEMMA: "sa"},
{ORTH: "'yo", LEMMA: "iyo"}],
"ano'ng": [
{ORTH: "ano", LEMMA: "ano"},
{ORTH: "'ng", LEMMA: "ang"}],
"siya'y": [
{ORTH: "siya", LEMMA: "siya"},
{ORTH: "'y", LEMMA: "ay"}],
"nawa'y": [
{ORTH: "nawa", LEMMA: "nawa"},
{ORTH: "'y", LEMMA: "ay"}],
"papa'no": [
{ORTH: "papa'no", LEMMA: "papaano"}],
"'di": [
{ORTH: "'di", LEMMA: "hindi"}]
}
# To keep things clean and readable, it's recommended to only declare the
# TOKENIZER_EXCEPTIONS at the bottom:
TOKENIZER_EXCEPTIONS = _exc