# Modeling Temporal Evidence from External Collections

## TL;DR

This paper introduces a novel time-aware retrieval model that leverages external collections to identify relevant time periods for events, enhancing search precision by integrating temporal evidence into multiple retrieval stages.

## Contribution

It proposes a formal retrieval model that uses external sources for temporal evidence, improving relevance estimation, query expansion, and result re-ranking in time-sensitive information retrieval.

## Key findings

- Improved search precision over recent temporal models.
- Strong correlation between relevance and temporal distribution.
- Effective use of external collections for temporal relevance inference.

## Abstract

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.