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