Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models

As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search. This interest has become particularly relevant as the energy consumption of information retrieval models has risen with new neural models based on large language models, leading to an associated increase of CO2 emissions, albeit relatively low compared to fields such as natural language processing

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited. This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.Comment: This paper is currently in submission with Intelligent Systems with Applications Journal Technology-Assisted Review Systems Special issue and is under peer review. arXiv admin note: text overlap with arXiv:2112.0027

Big Brother: A Drop-In Website Interaction Logging Service

Fine-grained logging of interactions in user studies is important for studying user behaviour, among other reasons. However, in many research scenarios, the way interactions are logged are usually tied to a monolithic system. We present a generic, application independent service for logging interactions in web-pages, specifically targeting user studies. Our service, Big Brother, can be dropped-in to existing user interfaces with almost no configuration required by researchers. Big Brother has already been used in several user studies to record interactions in a number of user study research scenarios, such as lab-based and crowdsourcing environments. We further demonstrate the ability for Big Brother to scale to very large user studies through benchmarking experiments. Big Brother also provides a number of additional tools for visualising and analysing interactions. Big Brother significantly lowers the barrier to entry for logging user interactions by providing a minimal but powerful, no configuration necessary, service for researchers and practitioners of user studies that can scale to thousands of concurrent sessions. We have made the source code and releases for Big Brother available for download at https://github.com/hscells/bigbro

Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation

Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. The goal is to prioritise the most important documents so that subsequent review steps can be carried out more efficiently and effectively. The current state of the art uses the final title of the review to rank documents using BERT-based neural neural rankers. However, the final title is only formulated at the end of the review process, which makes this approach impractical as it relies on ex post facto information. At the time of screening, only a rough working title is available, with which the BERT-based ranker achieves is significantly worse than the final title. In this paper, we explore alternative sources of queries for screening prioritisation, such as the Boolean query used to retrieve the set of documents to be screened, and queries generated by instruction-based generative large language models such as ChatGPT and Alpaca. Our best approach is not only practical based on the information available at screening time, but is similar in effectiveness with the final title.Comment: Preprints for Accepted paper in SIGIR-AP-202

Query variation performance prediction for systematic reviews

When conducting systematic reviews, medical researchers heavily deliberate over the final query to pose to the information retrieval system. Given the possible query variations that they could construct, selecting the best performing query is difficult. This motivates a new type of query performance prediction (QPP) task where the challenge is to estimate the performance of a set of query variations given a particular topic. Query variations are the reductions, expansions and modifications of a given seed query under the hypothesis that there exists some variations (either generated from permutations or hand crafted) which will improve retrieval effectiveness over the original query. We use the CLEF 2017 TAR Collection, to evaluate sixteen pre and post retrieval predictors for the task of Query Variation Performance Prediction (QVPP). Our findings show the IDF based QPPs exhibits the strongest correlations with performance. However, when using QPPs to select the best query, little improvement over the original query can be obtained, despite the fact that there are query variations which perform significantly better. Our findings highlight the difficulty in identifying effective queries within the context of this new task, and motivates further research to develop more accurate methods to help systematic review researchers in the query selection process

A test collection for evaluating retrieval of studies for inclusion in systematic reviews

This paper introduces a test collection for evaluating the effectiveness of different methods used to retrieve research studies for inclusion in systematic reviews. Systematic reviews appraise and synthesise studies that meet specific inclusion criteria. Systematic reviews intended for a biomedical science audience use boolean queries with many, often complex, search clauses to retrieve studies; these are then manually screened to determine eligibility for inclusion in the review. This process is expensive and time consuming. The development of systems that improve retrieval effectiveness will have an immediate impact by reducing the complexity and resources required for this process. Our test collection consists of approximately 26 million research studies extracted from the freely available MEDLINE database, 94 review (query) topics extracted from Cochrane systematic reviews, and corresponding relevance assessments. Tasks for which the collection can be used for information retrieval system evaluation are described and the use of the collection to evaluate common baselines within one such task is demonstrated. The test collection is available at https://github.com/ielab/SIGIR2017-PICO-Collection

The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017

As an empirical discipline, information access and retrieval research requires substantial software infrastructure to index and search large collections. This workshop is motivated by the desire to better align information retrieval research with the practice of building search applications from the perspective of open-source information retrieval systems. Our goal is to promote the use of Lucene for information access and retrieval research

The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives

The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries, 166 million search result pages, and 1.7 billion search results across 550 search providers. Although many query logs have been studied in the literature, the search providers that own them generally do not publish their logs to protect user privacy and vital business data. Of the few query logs publicly available, none combines size, scope, and diversity. The AQL is the first to do so, enabling research on new retrieval models and (diachronic) search engine analyses. Provided in a privacy-preserving manner, it promotes open research as well as more transparency and accountability in the search industry.Comment: SIGIR 2023 resource paper, 13 page

VRLE: Lifelog Interaction Prototype in Virtual Reality:Lifelog Search Challenge at ACM ICMR 2020

The Lifelog Search Challenge (LSC) invites researchers to share their prototypes for interactive lifelog retrieval and encourages competition to develop and evaluate effective methodologies to achieve this. With this paper we present a novel approach to visual lifelog exploration based on our research to date utilising virtual reality as a medium for interactive information retrieval. The VRLE prototype presented is an iteration on a previous system which won the first LSC competition at ACM ICMR 2018

Evaluating Generative Ad Hoc Information Retrieval

Recent advances in large language models have enabled the development of viable generative information retrieval systems. A generative retrieval system returns a grounded generated text in response to an information need instead of the traditional document ranking. Quantifying the utility of these types of responses is essential for evaluating generative retrieval systems. As the established evaluation methodology for ranking-based ad hoc retrieval may seem unsuitable for generative retrieval, new approaches for reliable, repeatable, and reproducible experimentation are required. In this paper, we survey the relevant information retrieval and natural language processing literature, identify search tasks and system architectures in generative retrieval, develop a corresponding user model, and study its operationalization. This theoretical analysis provides a foundation and new insights for the evaluation of generative ad hoc retrieval systems.Comment: 14 pages, 5 figures, 1 tabl