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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def _make_tagdict(self, sentences):
        '''Make a tag dictionary for single-tag words.'''
        counts = defaultdict(lambda: defaultdict(int))
        for words, tags in sentences:
            for word, tag in zip(words, tags):
                counts[word][tag] += 1
                self.classes.add(tag)
        freq_thresh = 20
        ambiguity_thresh = 0.97
        for word, tag_freqs in counts.items():
            tag, mode = max(tag_freqs.items(), key=lambda item: item[1])
            n = sum(tag_freqs.values())
            # Don't add rare words to the tag dictionary
            # Only add quite unambiguous words
            if n >= freq_thresh and (float(mode) / n) >= ambiguity_thresh:
                self.tagdict[word] = tag