Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.

Assessing Administrative and Political Factors in Implementing a Living Wage Ordinance

Since 2000, the number of living wage ordinances has steadily increased throughout the country. While most of the current research has focused on the beneficial outcomes of living wages, little has been published on their administrative practices. To address this shortcoming, this study focused on the identification of key administrative and political factors involved impacting the implementation of living wage ordinances in Miami-Dade and Broward Counties. The study utilized a triangulation of interviews, surveys, and direct observation. The author conducted interviews of administrators and members of the living wage oversight boards in both counties and observed the monthly meetings held by each county’s oversight board from January 2006 to June 2007. These findings were buttressed with a national survey of senior staff in other living wage communities. The study utilized descriptive statistics, Chi Square, Cronbach’s Alpha, and Spearman’s Rank Correlation Coefficient (Spearman’s rho). Interviews indicated that administrators in Dade and Broward are seriously under-staffed and budgeted. Ambiguities in the enabling ordinances have lead to loopholes that undermine implementation and accountability for participating contractors. Survey results showed that policy ambiguity, organizational politics, and a lack of organizational capacity were significant negative factors in the implementation process while an organizational culture emphasizing consistent enforcement was a positive factor. Without the proper inputs, an organization hinders itself from meeting its outputs and outcomes. This study finds that Broward and Miami-Dade Counties do not provide the necessary administrative support to implement a living wage effectively – in stark contrast to the high hopes and strong political support behind their passage. For a living wage to succeed, it first needs an organizational culture committed to providing the necessary resources for implementation as well as transparent, consistent accountability mechanisms.