Temporal Feedback for Tweet Search with Non-Parametric Density Estimation

This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feedback: starting with an initial set of results from a baseline retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Experiments on TREC datasets confirm that our temporal feedback formulation improves search effectiveness, thus providing support for our hypothesis. Our approach outperforms both a standard baseline and previous temporal retrieval models. Temporal feedback improves over standard lexical feedback (with and without human judgments), illustrating that temporal relevance signals exist independently of document content

New Collection Announcement: Focused Retrieval Over the Web

Focused retrieval (a.k.a., passage retrieval) is important at its own right and as an intermediate step in question answering systems. We present a new Web-based collection for focused retrieval. The document corpus is the Category A of the ClueWeb12 collection. Forty-nine queries from the educational domain were created. The 100 documents most highly ranked for each query by a highly effective learning-to-rank method were judged for relevance using crowdsourcing. All sentences in the relevant documents were judged for relevance