Generation of Infotips from Interface Labels

Abstract

A method is presented for generating informative and naturalsounding infotips for graphical elements of a user interface. A domain-specific corpus is prepared using natural language processing techniques, and a termfrequency/inverse-document-frequency transform is used for vectorization of features. A k-means algorithm is then used to cluster the corpus by semantic similarity and retrieve the most similar infotips for any inputted interface label. We demonstrate the feasibility of this method and conclude by proposing several approaches to improve the selection of infotips by incorporating natural language processing and machine learning techniques.