Centroid Terms as Text Representatives
Mario Kubek, Herwig Unger · 2016
Algorithms to topically cluster and classify texts rely on information about their semantic distances and similarities. Standard methods based on the bag-of-words model to determine this information return only rough estimations regarding the relatedness of texts. Moreover, they are per se unable to find generalising terms or abstractions describing the textual contents. A new method to determine centroid terms in texts and to evaluate their similarity using those representing terms will be introduced. In first experiments, its results and advantages will be discussed.