Inverse Sentence Frequency (ISF) is a measure designed to evaluate the importance of a word based on how frequently it appears across sentences. The underlying principle is that words appearing in many sentences are generally less informative regarding the specific content or themes of the text. Conversely, words that are present in fewer sentences are considered more significant as they likely pertain to more specific or unique aspects of the text.

ISF quantifies this concept by assigning higher scores to words with lower sentence distribution, thereby highlighting their potential value in characterizing the text.

Implementing ISF Calculation

The process of calculating ISF scores involves the following steps:

1. Utilizing Word Distribution Counts: The word_sentence_counts dictionary, prepared earlier, maps each word to the number of sentences it appears in. This data is essential for calculating ISF scores as it reflects the sentence-level distribution of words;

2. Applying the ISF Formula: For each word, the ISF score is calculated using a logarithmic scale. The formula log(len(sentences) / word_sentence_counts[word]) takes the total number of sentences in the text and divides it by the count of sentences containing the word.