In TREC 2011, we focus on tackling the new challenges proposed by the pilot Crowdsourcing and Microblog tracks, using our Terrier Information Retrieval Platform. Meanwhile, we continue to build upon our novel xQuAD framework and data-driven ranking approaches within Terrier to achieve effective and efficient ranking for the TREC Web track. In particular, the aim of our Microblog track participation is the development of a learning to rank approach for filtering within a tweet ranking

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... , where tweets are ranked in reverse chronological order. In the Crowdsourcing track, we work to achieve a closer integration between the crowdsourcing marketplaces that are used for relevance assessment, and Terrier, which produces the document rankings to be assessed. Moreover, we focus on generating relevance assessments quickly and at a minimal cost. For the Web track, we enhance the data-driven learning support within Terrier by proposing a novel framework for the fast computation of document features for learning to rank.