Textual Information Segmentation by Cohesive Ties

This paper proposes a novel approach in clustering texts automatically into coherent segments. A set of mutual linguistic constraints that largely determines the similarity of meaning among lexical items is used and a weight function is devised to incorporate the diversity of linguistic bonds among the text. A computational method of extracting the gist from a higher order structure representing the tremendous diversity of interrelationship among items is presented. Topic boundaries between segments in a text are identified. Our text segmentation is regarded as a process of identifying the shifts from one segment cluster to another. The experimental results show that the combination of these constraints is capable to address the topic shifts of texts.