Characterizing scientific contributions through automatic acknowledgement indexing and citation analysis.

摘要

Acknowledgements in research publications, like citations, indicate influential contributions to scientific work. However, acknowledgements are different from citations in an important regard; whereas citations are formal expressions of debt, acknowledgements are arguably more personal, singular, or private expressions of appreciation and contribution. Furthermore, many institutional sponsors of science expect researchers to acknowledge support that contributed to the completion of published work. Citation analysis has proved to be an important tool for evaluating research contributions; however, supplementing citation information with acknowledgements provides a more complete picture of communication and influence in science.; This dissertation reports the development of automated methods for acknowledgement identification and analysis in research publications. The methods were implemented within the CiteSeer Digital Library in order to produce the largest acknowledgement analysis to date by an order of magnitude. Acknowledgement data is supplemented by CiteSeer's automatically derived citation index in order to characterize the previously "hidden" impact of acknowledged entities, including funding agencies, corporations, educational institutions, and individuals. As the analysis of acknowledgements depends upon accurate and up-to-date citation indexing, a next-generation citation matching framework is presented which promises to increase the accuracy, precision, and timeliness of automatic citation indices.