A proposed new metric for the conceptual diversity of a text
This provides a tool for evaluating and comparing concept diversity in natural language processing and semantic research, addressing a previously unstandardized area.
The paper tackles the problem of measuring conceptual diversity in texts by introducing a standardized metric, demonstrating its application with example scores of 16.6801 for a high-diversity sentence and 3.9068 for a low-diversity one.
A word may contain one or more hidden concepts. While the "animal" word evokes many images in our minds and encapsulates many concepts (birds, dogs, cats, crocodiles, etc.), the `parrot' word evokes a single image (a colored bird with a short, hooked beak and the ability to mimic sounds). In spoken or written texts, we use some words in a general sense and some in a detailed way to point to a specific object. Until now, a text's conceptual diversity value cannot be determined using a standard and precise technique. This research contributes to the natural language processing field of AI by offering a standardized method and a generic metric for evaluating and comparing concept diversity in different texts and domains. It also contributes to the field of semantic research of languages. If we give examples for the diversity score of two sentences, "He discovered an unknown entity." has a high conceptual diversity score (16.6801), and "The endoplasmic reticulum forms a series of flattened sacs within the cytoplasm of eukaryotic cells." sentence has a low conceptual diversity score which is 3.9068.