def standardized_concept_uri(lang, text, *more):
    "\n    Make the appropriate URI for a concept in a particular language, including\n    removing English stopwords, normalizing the text in a way appropriate\n    to that language (using the text normalization from wordfreq), and joining\n    its tokens with underscores in a concept URI.\n\n    This text normalization can smooth over some writing differences: for\n    example, it removes vowel points from Arabic words, and it transliterates\n    Serbian written in the Cyrillic alphabet to the Latin alphabet so that it\n    can match other words written in Latin letters.\n\n    'more' contains information to distinguish word senses, such as a part\n    of speech or a WordNet domain. The items in 'more' get lowercased and\n    joined with underscores, but skip many of the other steps -- for example,\n    they won't have stopwords removed.\n\n    >>> standardized_concept_uri('en', 'this is a test')\n    '/c/en/this_is_test'\n    >>> standardized_concept_uri('en', 'this is a test', 'n', 'example phrase')\n    '/c/en/this_is_test/n/example_phrase'\n    >>> standardized_concept_uri('sh', 'симетрија')\n    '/c/sh/simetrija'\n    "
    lang = lang.lower()
    if (lang == 'en'):
        token_filter = english_filter
    else:
        token_filter = None
    text = preprocess_text(text.replace('_', ' '), lang)
    tokens = simple_tokenize(text)
    if (token_filter is not None):
        tokens = token_filter(tokens)
    norm_text = '_'.join(tokens)
    more_text = []
    for item in more:
        if (item is not None):
            tokens = simple_tokenize(item.replace('_', ' '))
            if (token_filter is not None):
                tokens = token_filter(tokens)
            more_text.append('_'.join(tokens))
    return concept_uri(lang, norm_text, *more_text)
Make the appropriate URI for a concept in a particular language, including
removing English stopwords, normalizing the text in a way appropriate
to that language (using the text normalization from wordfreq), and joining
its tokens with underscores in a concept URI.

This text normalization can smooth over some writing differences: for
example, it removes vowel points from Arabic words, and it transliterates
Serbian written in the Cyrillic alphabet to the Latin alphabet so that it
can match other words written in Latin letters.

'more' contains information to distinguish word senses, such as a part
of speech or a WordNet domain. The items in 'more' get lowercased and
joined with underscores, but skip many of the other steps -- for example,
they won't have stopwords removed.

>>> standardized_concept_uri('en', 'this is a test')
'/c/en/this_is_test'
>>> standardized_concept_uri('en', 'this is a test', 'n', 'example phrase')
'/c/en/this_is_test/n/example_phrase'
>>> standardized_concept_uri('sh', 'симетрија')
'/c/sh/simetrija'
def standardized_concept_uri(lang, text, *more):
    "\n    Make the appropriate URI for a concept in a particular language, including\n    removing English stopwords, normalizing the text in a way appropriate\n    to that language (using the text normalization from wordfreq), and joining\n    its tokens with underscores in a concept URI.\n\n    This text normalization can smooth over some writing differences: for\n    example, it removes vowel points from Arabic words, and it transliterates\n    Serbian written in the Cyrillic alphabet to the Latin alphabet so that it\n    can match other words written in Latin letters.\n\n    'more' contains information to distinguish word senses, such as a part\n    of speech or a WordNet domain. The items in 'more' get lowercased and\n    joined with underscores, but skip many of the other steps -- for example,\n    they won't have stopwords removed.\n\n    >>> standardized_concept_uri('en', 'this is a test')\n    '/c/en/this_is_test'\n    >>> standardized_concept_uri('en', 'this is a test', 'n', 'example phrase')\n    '/c/en/this_is_test/n/example_phrase'\n    >>> standardized_concept_uri('sh', 'симетрија')\n    '/c/sh/simetrija'\n    "
    lang = lang.lower()
    if (lang == 'en'):
        token_filter = english_filter
    else:
        token_filter = None
    text = preprocess_text(text.replace('_', ' '), lang)
    tokens = simple_tokenize(text)
    if (token_filter is not None):
        tokens = token_filter(tokens)
    norm_text = '_'.join(tokens)
    more_text = []
    for item in more:
        if (item is not None):
            tokens = simple_tokenize(item.replace('_', ' '))
            if (token_filter is not None):
                tokens = token_filter(tokens)
            more_text.append('_'.join(tokens))
    return concept_uri(lang, norm_text, *more_text)