Paper: Global Ranking via Data Fusion

ACL ID C10-2026
Title Global Ranking via Data Fusion
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

Global ranking, a new information re- trieval (IR) technology, uses a ranking model for cases in which there exist re- lationships between the objects to be ranked. In the ranking task, the ranking model is defined as a function of the properties of the objects as well as the relations between the objects. Existing global ranking approaches address the problem by “learning to rank”. In this paper, we propose a global ranking framework that solves the problem via data fusion. The idea is to take each re- trieved document as a pseudo-IR sys- tem. Each document generates a pseu- do-ranked list by a global function. The data fusion algorithm is then adapted to generate the final ranked list. Taking a biomedical information extraction task, namely, interactor normalization ta...