Abstract
We introduce Enlil, an information extraction system that discovers the institutional affiliations of authors in scholarly papers. Enlil consists of two steps: one that first identifies authors and affiliations using a conditional random field; and a second support vector machine that connects authors to their affiliations. We benchmark Enlil in three separate experiments drawn from three different sources: the ACL Anthology Corpus, the ACM Digital Library, and a set of cross-disciplinary scientific journal articles acquired by querying Google Scholar. Against a state-of-the-art production baseline, Enlil reports a statistically significant improvement in F_1 of nearly 10% (p « 0.01). In the case of multidisciplinary articles from Google Scholar, Enlil is benchmarked over both clean input (F_1 > 90%) and automatically-acquired input (F_1 > 80%).We have deployed Enlil in a case study involving Asian genomics research publication patterns to understand how government sponsored collaborative links evolve. Enlil has enabled our team to construct and validate new metrics to quantify the facilitation of research as opposed to direct publication.
Min-Yen Kan
Associate Professor
WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.