Document expansion has been shown to improve the effectiveness of information retrieval systems by augmenting documents' term probability estimates with those of similar documents, producing higher quality document representations. We propose a method to further improve document models by utilizing external collections as part of the document expansion process. Our approach is based on relevance modeling, a popular form of pseudo-relevance feedback; however, where relevance modeling is concerned with query expansion, we are concerned with document expansion. Our experiments demonstrate that the proposed model improves ad-hoc document retrieval effectiveness on a variety of corpus types, with a particular benefit on more heterogeneous collections of documents.

This work takes an in-depth look at the factors that affect manual classifications of 'temporally sensitive' information needs. We use qualitative and quantitative techniques to analyze 660 topics from the Text Retrieval Conference (TREC) previously used in the experimental evaluation of temporal retrieval models. Regression analysis is used to identify factors in previous manual classifications. We explore potential problems with the previous classifications, considering principles and guidelines for future work on temporal retrieval models.

In information retrieval evaluation, when presented with an effectiveness difference between two systems, there are three relevant questions one might ask. First, are the differences statistically significant? Second, is the comparison stable with respect to assessor differences? Finally, is the difference actually meaningful to a user? This paper tackles the last two questions about assessor differences and user preferences in the context of the newly-introduced tweet timeline generation task in the TREC 2014 Microblog track, where the system's goal is to construct an informative summary of non-redundant tweets that addresses the user's information need. Central to the evaluation methodology is human-generated semantic clusters of tweets that contain substantively similar information. We show that the evaluation is stable with respect to assessor differences in clustering and that user preferences generally correlate with effectiveness metrics even though users are not explicitly aware of the semantic clustering being performed by the systems. Although our analyses are limited to this particular task, we believe that lessons learned could generalize to other evaluations based on establishing semantic equivalence between information units, such as nugget-based evaluations in question answering and temporal summarization.

Entity-centric document filtering is the task of analyzing a time-ordered stream of documents and emitting those that are relevant to a specified set of entities (e.g., people, places, organizations). This task is exemplified by the TREC Knowledge Base Acceleration (KBA) track and has broad applicability in other modern IR settings. In this paper, we present a simple yet effective approach based on learning high-quality Boolean queries that can be applied deterministically during filtering. We call these Boolean statements sufficient queries. We argue that using deterministic queries for entity-centric filtering can reduce confounding factors seen in more familiar "score-then-threshold" filtering methods. Experiments on two standard datasets show significant improvements over state-of-the-art baseline models.