Incrementally Clustering Legislative Interpellation Documents

Abstract

The Parliamentary Library of Legislative Yuan website provides a fair and objective channel for the public to trace daily activities of the Legislative Yuan and legislators' inquiries in Taiwan. However, the increased information content causes information overloading problem. To mitigate such information overloading problem, this study proposes an incremental clustering mechanism to renew the information regularly by presenting it as a categorical structure to ease the efforts on tracing issue development. This study first initiates a basic categorical structure by a two-stage clustering approach. Then, the incremental clustering method is applied to clustering a collection of incoming documents corresponding to the same topic into clusters, and designates these clusters into existing categories or creates a new category. Experimental results show the effectiveness of the proposed incremental clustering method, which enables the management of the hierarchical structure of categories on legislative interpellation. This study contributes to e-government initiatives on facilitating the public to trace the legislative activities periodically.