def revise_paragraph_classification(paragraphs, max_heading_distance=MAX_HEADING_DISTANCE_DEFAULT):
    """
    Context-sensitive paragraph classification. Assumes that classify_pragraphs
    has already been called.
    """
    # copy classes
    for paragraph in paragraphs:
        paragraph.class_type = paragraph.cf_class

    # good headings
    for i, paragraph in enumerate(paragraphs):
        if not (paragraph.heading and paragraph.class_type == 'short'):
            continue
        j = i + 1
        distance = 0
        while j < len(paragraphs) and distance <= max_heading_distance:
            if paragraphs[j].class_type == 'good':
                paragraph.class_type = 'neargood'
                break
            distance += len(paragraphs[j].text)
            j += 1

    # classify short
    new_classes = {}
    for i, paragraph in enumerate(paragraphs):
        if paragraph.class_type != 'short':
            continue
        prev_neighbour = get_prev_neighbour(i, paragraphs, ignore_neargood=True)
        next_neighbour = get_next_neighbour(i, paragraphs, ignore_neargood=True)
        neighbours = set((prev_neighbour, next_neighbour))
        if neighbours == set(['good']):
            new_classes[i] = 'good'
        elif neighbours == set(['bad']):
            new_classes[i] = 'bad'
        # it must be set(['good', 'bad'])
        elif (prev_neighbour == 'bad' and get_prev_neighbour(i, paragraphs, ignore_neargood=False) == 'neargood') or \
             (next_neighbour == 'bad' and get_next_neighbour(i, paragraphs, ignore_neargood=False) == 'neargood'):
            new_classes[i] = 'good'
        else:
            new_classes[i] = 'bad'

    for i, c in new_classes.items():
        paragraphs[i].class_type = c

    # revise neargood
    for i, paragraph in enumerate(paragraphs):
        if paragraph.class_type != 'neargood':
            continue
        prev_neighbour = get_prev_neighbour(i, paragraphs, ignore_neargood=True)
        next_neighbour = get_next_neighbour(i, paragraphs, ignore_neargood=True)
        if (prev_neighbour, next_neighbour) == ('bad', 'bad'):
            paragraph.class_type = 'bad'
        else:
            paragraph.class_type = 'good'

    # more good headings
    for i, paragraph in enumerate(paragraphs):
        if not (paragraph.heading and paragraph.class_type == 'bad' and paragraph.cf_class != 'bad'):
            continue
        j = i + 1
        distance = 0
        while j < len(paragraphs) and distance <= max_heading_distance:
            if paragraphs[j].class_type == 'good':
                paragraph.class_type = 'good'
                break
            distance += len(paragraphs[j].text)
            j += 1