Modeling Temporal Evidence from External Collections
This work addresses the challenge of improving temporal relevance in event-based search for applications like social media analysis, though it is incremental by building on existing advances.
The paper tackled the problem of estimating the most relevant time periods for events by mining temporal evidence from external collections and proposing a formal retrieval model that uses this evidence to infer temporal relevance, select query expansion terms, and re-rank results. Experiments on TREC Microblog collections showed improved search results over recent temporal models, with a strong correlation between precision and temporal distribution.
Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.