preprocess package

This module deals with normalizing scripts and orthographies by using writing conventions based on dialects and scripts. The goal is not to correct the orthography but to normalize the text in terms of the encoding and common writing rules. The input encoding should be in UTF-8 only. To this end, three functions are provided as follows:

  • normalize: deals with different encodings and unifies characters based on dialects and scripts
  • standardize: given a normalized text, it returns standardized text based on the Kurdish orthographies following recommendations for Kurmanji and Sorani
  • unify_numerals: conversion of the various types of numerals used in Kurdish texts

It is recommended that the output of this module be used as the input of subsequent tasks in an NLP pipeline.

Examples:


>>> from klpt.preprocess import Preprocess

>>> preprocessor_ckb = Preprocess("Sorani", "Arabic", numeral="Latin")
>>> preprocessor_ckb.normalize("لە ســـاڵەکانی ١٩٥٠دا")
'لە ساڵەکانی 1950دا'
>>> preprocessor_ckb.standardize("راستە لەو ووڵاتەدا")
'ڕاستە لەو وڵاتەدا'
>>> preprocessor_ckb.unify_numerals("٢٠٢٠")
'2020'

>>> preprocessor_kmr = Preprocess("Kurmanji", "Latin")
>>> preprocessor_kmr.standardize("di sala 2018-an")
'di sala 2018an'
>>> preprocessor_kmr.standardize("hêviya")
'hêvîya'

The preprocessing rules are provided at data/preprocess_map.json.

__init__(self, dialect, script, numeral='Latin') special

Initialization of the Preprocess class

Parameters:

Name Type Description Default
dialect str

the name of the dialect or its ISO 639-3 code

required
script str

the name of the script

required
numeral str

the type of the numeral

'Latin'
Source code in klpt/preprocess.py
def __init__(self, dialect, script, numeral="Latin"):
    """
    Initialization of the Preprocess class

    Arguments:
        dialect (str): the name of the dialect or its ISO 639-3  code
        script (str): the name of the script
        numeral (str): the type of the numeral

    """
    with open(klpt.get_data("data/preprocess_map.json")) as preprocess_file:
        self.preprocess_map = json.load(preprocess_file)

    configuration = Configuration({"dialect": dialect, "script": script, "numeral": numeral})
    self.dialect = configuration.dialect
    self.script = configuration.script
    self.numeral = configuration.numeral

normalize(self, text)

Text normalization

This function deals with different encodings and unifies characters based on dialects and scripts as follows:

  • Sorani-Arabic:

    • replace frequent Arabic characters with their equivalent Kurdish ones, e.g. "ي" by "ی" and "ك" by "ک"
    • replace "ه" followed by zero-width non-joiner (ZWNJ, U+200C) with "ە" where ZWNJ is removed ("ره‌زبه‌ر" is converted to "رەزبەر"). ZWNJ in HTML is also taken into account.
    • replace "هـ" with "ھ" (U+06BE, ARABIC LETTER HEH DOACHASHMEE)
    • remove Kashida "ـ"
    • "ھ" in the middle of a word is replaced by ه (U+0647)
    • replace different types of y, such as 'ARABIC LETTER ALEF MAKSURA' (U+0649)

It should be noted that the order of the replacements is important. Check out provided files for further details and test cases.

Parameters:

Name Type Description Default
text str

a string

required

Returns:

Type Description
str

normalized text

Source code in klpt/preprocess.py
def normalize(self, text):
    """
    Text normalization

    This function deals with different encodings and unifies characters based on dialects and scripts as follows:

    - Sorani-Arabic:

        - replace frequent Arabic characters with their equivalent Kurdish ones, e.g. "ي" by "ی" and "ك" by "ک"
        - replace "ه" followed by zero-width non-joiner (ZWNJ, U+200C) with "ە" where ZWNJ is removed ("ره‌زبه‌ر" is converted to "رەزبەر"). ZWNJ in HTML is also taken into account.
        - replace "هـ" with "ھ" (U+06BE, ARABIC LETTER HEH DOACHASHMEE)
        - remove Kashida "ـ"
        - "ھ" in the middle of a word is replaced by ه (U+0647)
        - replace different types of y, such as 'ARABIC LETTER ALEF MAKSURA' (U+0649)

    It should be noted that the order of the replacements is important. Check out provided files for further details and test cases.

    Arguments:
        text (str): a string

    Returns:
        str: normalized text

     """
    temp_text = " " + self.unify_numerals(text) + " "

    for normalization_type in ["universal", self.dialect]:
        for rep in self.preprocess_map["normalizer"][normalization_type][self.script]:
            rep_tar = self.preprocess_map["normalizer"][normalization_type][self.script][rep]
            temp_text = re.sub(rf"{rep}", rf"{rep_tar}", temp_text, flags=re.I)

    return temp_text.strip()

preprocess(self, text)

One single function for normalization, standardization and unification of numerals

Parameters:

Name Type Description Default
text str

a string

required

Returns:

Type Description
str

preprocessed text

Source code in klpt/preprocess.py
def preprocess(self, text):
    """
    One single function for normalization, standardization and unification of numerals

    Arguments:
        text (str): a string

    Returns:
        str: preprocessed text
    """
    return self.unify_numerals(self.standardize(self.normalize(text)))

standardize(self, text)

Method of standardization of Kurdish orthographies

Given a normalized text, it returns standardized text based on the Kurdish orthographies.

  • Sorani-Arabic:

    • replace alveolar flap ر (/ɾ/) at the begging of the word by the alveolar trill ڕ (/r/)
    • replace double rr and ll with ř and ł respectively
  • Kurmanji-Latin:

    • replace "-an" or "'an" in dates and numerals ("di sala 2018'an" and "di sala 2018-an" -> "di sala 2018an")

Open issues: - replace " وە " by " و "? But this is not always possible, "min bo we" (ریزگـرتنا من بو وە نە ئە وە ئــە ز) - "pirtükê": "pirtûkê"? - Should ı (LATIN SMALL LETTER DOTLESS I be replaced by i?

Parameters:

Name Type Description Default
text str

a string

required

Returns:

Type Description
str

standardized text

Source code in klpt/preprocess.py
def standardize(self, text):
    """
    Method of standardization of Kurdish orthographies

    Given a normalized text, it returns standardized text based on the Kurdish orthographies.

    - Sorani-Arabic:
        - replace alveolar flap ر (/ɾ/) at the begging of the word by the alveolar trill ڕ (/r/)
        - replace double rr and ll with ř and ł respectively

    - Kurmanji-Latin:
        - replace "-an" or "'an" in dates and numerals ("di sala 2018'an" and "di sala 2018-an" -> "di sala 2018an")

    Open issues:
        - replace " وە " by  " و "? But this is not always possible, "min bo we" (ریزگـرتنا من بو وە  نە ئە وە ئــە ز)
        - "pirtükê": "pirtûkê"?
        - Should [ı (LATIN SMALL LETTER DOTLESS I](https://www.compart.com/en/unicode/U+0131) be replaced by i?

    Arguments:
        text (str): a string

    Returns:
        str: standardized text

    """
    temp_text = " " + self.unify_numerals(text) + " "

    for standardization_type in [self.dialect]:
        for rep in self.preprocess_map["standardizer"][standardization_type][self.script]:
            rep_tar = self.preprocess_map["standardizer"][standardization_type][self.script][rep]
            temp_text = re.sub(rf"{rep}", rf"{rep_tar}", temp_text, flags=re.I)

    return temp_text.strip()

unify_numerals(self, text)

Convert numerals to the desired one

There are three types of numerals: - Arabic [١٢٣٤٥٦٧٨٩٠] - Farsi [۱۲۳۴۵۶۷۸۹۰] - Latin [1234567890]

Parameters:

Name Type Description Default
text str

a string

required

Returns:

Type Description
str

text with unified numerals

Source code in klpt/preprocess.py
def unify_numerals(self, text):
    """
    Convert numerals to the desired one

    There are three types of numerals:
    - Arabic [١٢٣٤٥٦٧٨٩٠]
    - Farsi [۱۲۳۴۵۶۷۸۹۰]
    - Latin [1234567890]

    Arguments:
        text (str): a string

    Returns:
        str: text with unified numerals

    """
    for i, j in self.preprocess_map["normalizer"]["universal"]["numerals"][self.numeral].items():
        text = text.replace(i, j)
    return text