Towards explainable ai in text features engineering for concept recognition

Abstract

The rapid and pervasive development of methods from Artificial Intelligence (AI) affects our everyday life. Its application improves the users’ experience of many daily tasks. Despite the enhancements provided, such approaches have a substantial limitation in the shortfall of people’s trust connected with their lack of explainability. In natural language understanding (NLU) and processing (NLP), a fundamental objective is to support human interactions using sense-making of the language for communication. Such methods try to comprehend and reproduce the self-evident processes of human communication. This applies either in receiving speech signals or in extracting relevant information from a text. Furthermore, the pervasiveness of AI methods in the workplace and on the free time demands a sustainable and verified support of users’ trust, as a natural condition for their acceptance. The objective of this work is to introduce a framework for the calculation and selection of understandable text features. Such features can increase the confidence placed into adopted NLP solutions. The following work outlines the Text Feature Framework and its text features, based on statistical information coming from a general text corpus. The showcase experiment uses those features to verify them on the concept recognition task. The results shows their capability to explain a model and its predictions. The resulting concept recognition models are competitive with other methods existing in the literature. It has the definitive advantage of being able to externalize the supporting evidence for a choice of concept identification.