def lemmatiser(list_of_words, tag, translated_option, lem_instance=False,
    preserve_case=False):
    """
    Take a list of unicode words and a tag and return a lemmatised list
    """
    from corpkit.dictionaries.word_transforms import wordlist, taglemma
    if not lem_instance:
        from nltk.stem.wordnet import WordNetLemmatizer
        lem_instance = WordNetLemmatizer()
    output = []
    for word in list_of_words:
        if 'u' in translated_option:
            word = taglemma.get(word.lower(), 'Other')
        else:
            word = wordlist.get(word, lem_instance.lemmatize(word, tag))
        output.append(word)
    return output
