Unsupervised Rank Aggregation with Domain-Specific Expertise
نویسندگان
چکیده
Consider the setting where judges are repeatedly asked to (partially) rank sets of objects, and assume that each judge tries to reproduce some true underlying ranking to the best of their ability. Rank aggregation aims to combine the rankings of such experts to produce a better joint ranking, and is a ubiquitous problem in Information Retrieval (IR) and Natural Language Processing (NLP). In IR, for instance, meta-search aims to combine the outputs of multiple search engines. In machine translation (MT), aggregation of multiple systems built on different underlying principles has received considerable attention recently (e.g. [5]).
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