Paper: Collaborative Ranking: A Case Study on Entity Linking

ACL ID D11-1071
Title Collaborative Ranking: A Case Study on Entity Linking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

In this paper, we present a new ranking scheme, collaborative ranking (CR). In con- trast to traditional non-collaborative ranking scheme which solely relies on the strengths of isolated queries and one stand-alone rank- ing algorithm, the new scheme integrates the strengths from multiple collaborators of a query and the strengths from multiple ranking algorithms. We elaborate three specific forms of collaborative ranking, namely, micro col- laborative ranking (MiCR), macro collabora- tive ranking (MaCR) and micro-macro collab- orative ranking (MiMaCR). Experiments on entity linking task show that our proposed scheme is indeed effective and promising.