updates abbr
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@ -186,10 +186,21 @@ protected_phrases1 = [
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replacements = {'uni versity':'university',
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replacements = {'nat.':'national',
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'uni versity':'university',
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'inst ':'institute ',
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'adv ':'advanced ',
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'univ ':'university ',
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'stud ': 'studies ',
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'inst.':'institute',
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'adv.':'advanced',
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'univ.':'university',
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'stud.': 'studies',
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'uni versity':'university',
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'univ ':'university ',
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'univercity':'university',
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'universtiy':'university',
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'universtiy':'university',
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'univerisity':'university',
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'univeristy':'university',
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'universirty':'university',
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'universiti':'university',
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@ -234,7 +245,7 @@ def substrings_dict(string):
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dict_string = {}
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index = 0
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for value in split_strings:
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vaule = value.replace('.', ' ')
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# Check if the substring contains any university-related terms
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if not any(term in value.lower() for term in university_terms):
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# Apply regex substitutions for common patterns
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@ -286,7 +297,7 @@ def clean_string(input_string):
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# Remove characters that are not from the Latin alphabet, or allowed punctuation
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result = replace_comma_spaces(re.sub(r'[^a-zA-Z\s,;/]', '', result).strip())
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result = replace_comma_spaces(re.sub(r'[^a-zA-Z\s,;/.]', '', result).strip())
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# Restore the " - " sequence from the placeholder
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result = result.replace(placeholder, " – ")
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@ -308,7 +319,7 @@ def clean_string_facts(input_string):
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result = re.sub(r'\bsaint\b', 'st', result)
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# Remove characters that are not from the Latin alphabet or numbers
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result = re.sub(r'[^a-zA-Z0-9\s;/-]', '', result)
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result = re.sub(r'[^a-zA-Z0-9\s;/-.]', '', result)
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# Replace consecutive whitespace with a single space
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result = re.sub(r'\s+', ' ', result)
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