A team of six assessors used Dynamic Sampling (Cormack and Grossman 2018) and one hour of assessment effort per topic to form, without pooling, a test collection for the TREC 2018 Common Core Track. Later, official relevance assessments were rendered by NIST for documents selected by depth-10 pooling augmented by move-to-front (MTF) pooling (Cormack et al. 1998), as well as the documents selected by our Dynamic Sampling effort. MAP estimates rendered from dynamically sampled assessments using the xinfAP statistical evaluator are comparable to those rendered from the complete set of official assessments using the standard trec_eval tool. MAP estimates rendered using only documents selected by pooling, on the other hand, differ substantially. The results suggest that the use of Dynamic Sampling without pooling can, for an order of magnitude less assessment effort, yield information-retrieval effectiveness estimates that exhibit lower bias, lower error, and comparable ability to rank system effectiveness.

High-recall retrieval --- finding all or nearly all relevant documents --- is critical to applications such as electronic discovery, systematic review, and the construction of test collections for information retrieval tasks. The effectiveness of current methods for high-recall information retrieval is limited by their reliance on human input, either to generate queries, or to assess the relevance of documents. Past research has shown that humans can assess the relevance of documents faster and with little loss in accuracy by judging shorter document surrogates, e.g.\ extractive summaries, in place of full documents. To test the hypothesis that short document surrogates can reduce assessment time and effort for high-recall retrieval, we conducted a 50-person, controlled, user study. We designed a high-recall retrieval system using continuous active learning (CAL) that could display either full documents or short document excerpts for relevance assessment. In addition, we tested the value of integrating a search engine with CAL. In the experiment, we asked participants to try to find as many relevant documents as possible within one hour. We observed that our study participants were able to find significantly more relevant documents when they used the system with document excerpts as opposed to full documents. We also found that allowing participants to compose and execute their own search queries did not improve their ability to find relevant documents and, by some measures, impaired performance. These results suggest that for high-recall systems to maximize performance, system designers should think carefully about the amount and nature of user interaction incorporated into the system.

The goal of high-recall information retrieval (HRIR) is to find all or nearly all relevant documents for a search topic. In this paper, we present the design of our system that affords efficient high-recall retrieval. HRIR systems commonly rely on iterative relevance feedback. Our system uses a state-of-the-art implementation of continuous active learning (CAL), and is designed to allow other feedback systems to be attached with little work. Our system allows users to judge documents as fast as possible with no perceptible interface lag. We also support the integration of a search engine for users who would like to interactively search and judge documents. In addition to detailing the design of our system, we report on user feedback collected as part of a 50 participants user study. While we have found that users find the most relevant documents when we restrict user interaction, a majority of participants prefer having flexibility in user interaction. Our work has implications on how to build effective assessment systems and what features of the system are believed to be useful by users.

We present a system for identifying interesting social media posts on Twitter and delivering them to users' mobile devices in real time as push notifications. In our problem formulation, users are interested in broad topics such as politics, sports, and entertainment: our system processes tweets in real time to identify relevant, novel, and salient content. There are three interesting aspects to our work: First, instead of attempting to tame the cacophony of unfiltered tweets, we exploit a smaller, but still sizeable, collection of curated tweet streams corresponding to the Twitter accounts of different media outlets. Second, we apply distant supervision to extract topic labels from curated streams that have a specific focus, which can then be leveraged to build high-quality topic classifiers essentially "for free". Finally, our system delivers content via Twitter direct messages, supporting in situ interactions modeled after conversations with intelligent agents. These ideas are demonstrated in an end-to-end working prototype.