Development - Advanced, exercise 1

Text

The automated readability index (ARI) is a readability test for English texts, designed to gauge the understandability of a text by representing the US grade level needed to comprehend such text. The formula for calculating the ARI is the following one:

where chars is the number of letters and numbers, words is the number of token, and sentences is the number of sentences. Non-integer scores are always rounded up to the nearest whole number, so a score of 10.1 or 10.6 would be converted to 11.

Write the body of a Python function which takes a string representing a text in input and returns the ARI for that text. As a simplification, the input text can be composed only by English characters, numbers, commas, semicolons, colons, and full stops, and no abbreviation (such as “e.g.”) can be used.

Solution

from math import ceil
from re import sub


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


# Code of the function
def ari(text):
    num_char = len(sub("[^A-z0-9]", "", text))
    num_word = len(text.split())
    num_sent = len(sub("[^\\.]", "", text))
    ari_value = (4.71 * (num_char / num_word)) + (0.5 * (num_word / num_sent)) - 21.43
    return ceil(ari_value)


# Tests
print(test_ari("This is just a string.", -2))
print(test_ari("Semantic Publishing involves the use of Web and Semantic Web technologies and standards for the " \
               "semantic enhancement of a scholarly work so as to improve its discoverability, interactivity, " \
               "openness and (re-)usability for both humans and machines. Recently, people have suggested that " \
               "the semantic enhancements of a scholarly work should be undertaken by the authors of that scholarly " \
               "work, and should be considered as integral parts of the contribution subjected to peer review. " \
               "However, this requires that the authors should spend additional time and effort adding such semantic " \
               "annotations, time that they usually do not have available. Thus, the most pragmatic way to " \
               "facilitate this additional task is to use automated services that create the semantic annotation " \
               "of authors' scholarly articles by parsing the content that they have already written, thus reducing " \
               "the additional time required of the authors to that for checking and validating these semantic " \
               "annotations. In this article, I propose a generic approach called compositional and iterative " \
               "semantic enhancement (CISE) that enables the automatic enhancement of scholarly papers with " \
               "additional semantic annotations in a way that is independent of the markup used for storing " \
               "scholarly articles and the natural language used for writing their content.", 25))

Additional material

The runnable Python file is available online.