International Journal of Innovative Technology and Research
Abstract
A sentence is an integral unit of semantic nature, context and significance. Visualizing sentences for each topic is an important way to investigate and interpret unstructured corporate texts in subject modeling. Usually the term mining method is double: mining phrases and modeling theme. Current methods also suffer from order-sensitive and improper segmentation problems for phrase mining, which often lead to phrases of low content. The limitations of sentences, which may undermine continuity, are not entirely taken into account by standard topic models for topic modeling. In addition, current methods are frequently subject to domain terminology loss as the effect of topical domain dissemination is disregarded. We suggest an effective approach for high-quality and coherent topical sentence mining in this article. A high-quality sentence must meet the requirements for frequency, phrasing, integrity and suitability. In order to increase the both phrase consistency and topical cohesion, we combine the quality assured phrase mining process, a novel subject models that incorporate phrasing restriction, and a novel text clustering method into an iterative system. Effective algorithm designs to perform these methods effectively are often defined
