Systematic reviews are vital for evidence-based decision-making but remain resource-intensive due to the volume of literature requiring expert screening.Technology-Assisted Review (TAR) systems offer a solution by ranking documents for review, yet questions remain about how best to allocate limited human effort across multiple review topics.In this paper, we explore the problem of effort distribution by comparing alternative screening policies under fixed effort constraints.Using real-world data from the CLEF eHealth 2017-2019 TAR tasks, we evaluate both baseline and adaptive policies that account for topic size, screening depth, and residual uncertainty.We introduce effort-aware evaluation metrics to measure trade-offs between review effectiveness and resource use.Our results show that simple, topic-sensitive policies can significantly improve the yield of relevant documents discovered, offering practical insights for scalable and equitable systematic review workflows.

The Precision Medicine (PM) track at the Text REtrieval Conference (TREC) focuses on providing useful precision medicine-related information to clinicians treating cancer patients. The PM track gives the unique opportunity to evaluate medical IR systems using the same set of topics on two different collections: scientific literature and clinical trials. In the paper, we take advantage of this opportunity and we propose and evaluate state-of-the-art query expansion and reduction techniques to identify whether a particular approach can be helpful in both scientific literature and clinical trial retrieval. We present those approaches that are consistently effective in both TREC editions and we compare the results obtained with the best performing runs submitted to TREC PM 2017 and 2018.

In this demo, we present a web application which allows users to interact with two retrieval models, namely the Binary Independence Model (BIM) and the BM25 model, on a standard TREC collection. The goal of this demo is to give students deeper insight into the consequences of modeling assumptions (BIM vs. BM25) and the consequences of tuning parameter values by means of a two-dimensional representation of probabilities. The application was developed in R, and it is accessible at the following link: http://gmdn.shinyapps.io/shinyRF04.

Digital Geolinguistic systems encourage collaboration between linguists, historians, archaeologists, ethnographers, as they explore the relationship between language and cultural adaptation and change. In this demo, we propose a Linked Open Data approach for increasing the level of interoperability of geolinguistic applications and the reuse of the data. We present a case study of a geolinguistic project named Atlante Sintattico d'Italia, Syntactic Atlas of Italy (ASIt).

Naive-Bayes (NB) classifiers are simple probabilistic classifiers still widely used in supervised learning due to their tradeoff between efficient model training and good empirical results. One of the drawbacks of these classifiers is that in situations of data sparsity (i.e. when the size of training set is small) the maximum likelihood estimation of the probability of unseen features in these situations is equal to zero causing arithmetic anomalies. To prevent this undesirable behavior, a number of smoothing techniques have been proposed. Among these, the Bayesian approach incorporates smoothing in terms of prior knowledge about the parameters of the model usually called hyper-parameters. Our research question is: can a visualization tool help researchers to quickly assess the goodness of the performance of NB classifiers by setting optimal smoothing parameters?