Development - Advanced, exercise 18

Text

Bibliograms are verbal constructs made when noun phrases included in a text are ranked high to low by their frequency of co-occurrence with one or more user-supplied seed terms. Each bibliogram has three components:

• a seed term that sets a context;
• tokens (i.e. strings separated by a space) that co-occur (i.e. precede and follow) with the seed across the text;
• counts (frequencies) by which co-occurring tokens can be ordered high to low.

For instance, let us consider the following text:

After a while, finding that nothing more happened, she decided on going into the garden at once; but, alas for poor Alice! when she got to the door, she found she had forgotten the little golden key, and when she went back to the table for it, she found she could not possibly reach it: she could see it quite plainly through the glass, and she tried her best to climb up one of the legs of the table, but it was too slippery; and when she had tired herself out with trying, the poor little thing sat down and cried.

The bibliogram of the seed term “she” considering the text above is represented by the following list of tuples, each defining the token that co-occur with “she” accompanied by the related co-occurrence frequency: [("found", 4), ("when", 3), ("had", 2), ("could", 2), ("went", 1), ("happened,", 1), ("decided", 1), ("door,", 1), ("it,", 1), ("it:", 1), ("and", 1), ("tried", 1), ("got", 1)].

Write an algorithm in Python – def bibliogram(text, seed) – which returns the bibliogram of the seed given the input text.

Solution

# Test case for the function
def test_bibliogram(text, seed, expected):
    result = bibliogram(text, seed)
    if expected == result:
        return True
    else:
        return False


# Code of the function
def bibliogram(text, seed):
    result = {}
    split_text = text.split(" ")
    n_tokens = len(split_text)
    
    for idx, token in enumerate(split_text):
        if seed == token:
            if idx > 0:
                t = split_text[idx - 1]
                sum_one(t, result)

            if idx < n_tokens - 1:
                t = split_text[idx + 1]
                sum_one(t, result)
    
    final_list = list()
    for key in result:
        value = result[key]
        idx = get_idx(final_list, value)
        final_list.insert(idx, (key, value))
    
    return final_list


def get_idx(lst, v):
    idx = 0

    for idx, item in enumerate(lst):
        if v > item[1]:
            return idx
        else:
            idx = idx + 1
    
    return idx


def sum_one(t, result):
    if t not in result:
        result[t] = 0
    result[t] = result[t] + 1


# Tests
text = "After a while, finding that nothing more happened, she decided on going into the garden at once; but, alas for poor Alice! when she got to the door, she found she had forgotten the little golden key, and when she went back to the table for it, she found she could not possibly reach it: she could see it quite plainly through the glass, and she tried her best to climb up one of the legs of the table, but it was too slippery; and when she had tired herself out with trying, the poor little thing sat down and cried."
print(test_bibliogram(text, "he", []))
print(test_bibliogram(text, "on", [("decided", 1), ("going", 1)]))
print(test_bibliogram(text, "she", [("found", 4), ("when", 3), ("had", 2), ("could", 2), ("happened,", 1), ("decided", 1), ("got", 1), ("door,", 1), ("went", 1), ("it,", 1), ("it:", 1), ("and", 1), ("tried", 1)]))

Additional material

The runnable Python file is available online.