Tokenize a sentence and yields tuples of (word, start, end)

        Parameter:
            - sentence: the str(unicode) to be segmented.
            - mode: "default" or "search", "search" is for finer segmentation.
            - HMM: whether to use the Hidden Markov Model.

def tokenize(self, unicode_sentence, mode="default", HMM=True):
        """
        Tokenize a sentence and yields tuples of (word, start, end)

        Parameter:
            - sentence: the str(unicode) to be segmented.
            - mode: "default" or "search", "search" is for finer segmentation.
            - HMM: whether to use the Hidden Markov Model.
        """
        if not isinstance(unicode_sentence, text_type):
            raise ValueError("jieba: the input parameter should be unicode.")
        start = 0
        if mode == 'default':
            for w in self.cut(unicode_sentence, HMM=HMM):
                width = len(w)
                yield (w, start, start + width)
                start += width
        else:
            for w in self.cut(unicode_sentence, HMM=HMM):
                width = len(w)
                if len(w) > 2:
                    for i in xrange(len(w) - 1):
                        gram2 = w[i:i + 2]
                        if self.FREQ.get(gram2):
                            yield (gram2, start + i, start + i + 2)
                if len(w) > 3:
                    for i in xrange(len(w) - 2):
                        gram3 = w[i:i + 3]
                        if self.FREQ.get(gram3):
                            yield (gram3, start + i, start + i + 3)
                yield (w, start, start + width)
                start += width