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Although citizens agree on the importance of objective scientific information, yet they tend to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Instead, they rely on shallow information on the web or social media often published for commercial or political incentives rather than the correctness and informational value. This paper presents an overview of the CLEF 2022 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.

In Italy, the presence of music-oriented secondary schools (SMIM) represents a valuable opportunity to enhance the use of music in order to raise the level of inclusiveness of each institution. Music fits positively into the dialectic between education and inclusion as it is a significant developmental experience that enhances the participation and learning of pupils with all types of special educational needs and makes the entire community more welcoming. Indeed, it has been demonstrated that musical activity promotes the integral development of the person even in the presence of severe disabilities and acts as a true “neuroprotector” of language, improving working memory, attention, well-being and self-esteem. Within this framework, the volume presents the results of research funded by the Department of Educational Sciences of Roma Tre University and carried out in collaboration with the National Committee for the Practical Learning of Music for All Students of the Education Ministry. Using an integrated methodology, for the first time in Italy researchers have investigated the level of inclusiveness of SMIMs and, starting from the identification of the problematic nodes, elaborated a series of didactic-assessment proposals for improvement which have been shared with schools and are described in this volume. Some attachments are available for downloading and printing on the volume's web page, which can be accessed from the website https://series.francoangeli.it/index.php/oa.

LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest Y = {y1 , ..., yn } in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting; this is the first time that an evaluation exercise solely dedicated to quantification is organized. For both the binary setting and the single-label multiclass setting, data were provided to participants both in ready-made vector form and in raw document form. In this overview article we describe the structure of the lab, we report the results obtained by the participants in the four proposed tasks and subtasks, and we comment on the lessons that can be learned from these results.

The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2022 activities of the research group.

The Saerens-Latinne-Decaestecker (SLD) algorithm is a method whose goal is improving the quality of the posterior probabilities (or simply "posteriors") returned by a probabilistic classifier in scenarios characterized by prior probability shift (PPS) between the training set and the unlabelled ("test") set. This is an important task, (a) because posteriors are of the utmost importance in downstream tasks such as, e.g., multiclass classification and cost-sensitive classification, and (b) because PPS is ubiquitous in many applications. In this paper we explore whether using SLD can indeed improve the quality of posteriors returned by a classifier trained via active learning (AL), a class of machine learning (ML) techniques that indeed tend to generate substantial PPS. Specifically, we target AL via relevance sampling (ALvRS) and AL via uncertainty sampling (ALvUS), two AL techniques that are very well-known especially because, due to their low computational cost, are suitable to being applied in scenarios characterized by large datasets. We present experimental results obtained on the RCV1-v2 dataset, showing that SLD fails to deliver better-quality posteriors with both ALvRS and ALvUS, thus contradicting previous findings in the literature, and that this is due not to the amount of PPS that these techniques generate, but to how the examples they prioritize for annotation are distributed.

The 1st International Workshop on Learning to Quantify (LQ 2021 - https://cikmlq2021.github.io/), organized as a satellite event of the 30th ACM International Conference on Knowledge Management (CIKM 2021), took place on two separate days, November 1 and 5, 2021. As the main CIKM 2021 conference, the workshop was held entirely online, due to the COVID-19 pandemic. This report presents a summary of each keynote speech and contributed paper presented in this event, and discusses the issues that were raised during the workshop.

The 2nd International Workshop on Learning to Quantify (LQ 2022 - https: //lq-2022.github.io/) was held in Grenoble, FR, on September 23, 2022, as a satellite workshop of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2022). While the 1st edition of the workshop (LQ 2021 - https://cikmlq2021. github.io/, which was instead co-located with the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)) had to be an entirely online event, LQ 2022 was a hybrid event, with presentations given in-presence and both in-presence attendees and remote attendees. The workshop was a half-day event, and consisted of a keynote talk by Marco Saerens (Universit ?e Catholique de Louvain), presentations of four con- tributed papers, and a final collective discussion on the open problems of learning to quantify and on future initiatives. The present volume contains the four contributed papers that were ac- cepted for presentation at the workshop. Each of these papers was submitted as a response to the call for papers, was reviewed by at least three members of the international program committee, and was revised by the authors so as to take into account the feedback provided by the reviewers. We hope that the availability of the present volume will increase the interest in the subject of quantification on the part of researchers and practitioners alike, and will contribute to making quantification better known to potential users of this technology and to researchers interested in advancing the field.

Since 2000, the Conference and Labs of the Evaluation Forum (CLEF) has played a leading role in stimulating research and innovation in the domain of multimodal and multilingual information access. Initially founded as the Cross-Language Evaluation Forum and running in conjunction with the European Conference on Digital Libraries (ECDL/TPDL), CLEF became a standalone event in 2010 combining a peer-reviewed conference with a multi-track evaluation forum. The combination of the scientific pro- gram and the track-based evaluations at the CLEF conference creates a unique platform to explore information access from different perspectives, in any modality and language.

Modern information access systems hold the promise to give users direct access to key information from authoritative primary sources such as scientific literature, but non-experts tend to avoid these sources due to their complex language, internal vernacular, or lacking prior background knowledge. Text simplification approaches can remove some of these barriers, thereby avoiding that users rely on shallow information in sources prioritizing commercial or political incentives rather than the correctness and informational value. The CLEF 2021 SimpleText track will address the opportunities and challenges of text simplification approaches to improve scientific information access head-on. We aim to provide appropriate data and benchmarks, starting with pilot tasks in 2021, and create a community of NLP and IR researchers working together to resolve one of the greatest challenges of today.

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This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more.

"Aoccdrnig to a reasrech at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny itmopnrat tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe". We investigate the extent to which this phenomenon applies to computers as well. Our hypothesis is that computers are able to learn distributed word representations that are resilient to character reshuffling, without incurring a significant loss in performance in tasks that use these representations. If our hypothesis is confirmed, this may form the basis for a new and more efficient way of encoding character-based representations of text in deep learning, and one that may prove especially robust to misspellings, or to corruption of text due to OCR. This paper discusses some fundamental psycho-linguistic aspects that lie at the basis of the phenomenon we investigate, and reports on a preliminary proof of concept of the above idea.

The Artificial Intelligence for Media and Humanities laboratory (AIMH) has the mission to investigate and advance the state of the art in the Artificial Intelligence field, specifically addressing applications to digital media and digital humanities, and taking also into account issues related to scalability. This report summarize the 2021 activities of the research group.

Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user’s explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification.To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research.

Background Up to 10% of individuals with breast cancer (BC) belong to families with hereditary syndromes. The aim of this study was to develop an instrument to identify individuals/families at high‐hereditary risk for BC and offer dedicated surveillance programs according to different risks. Methods The instrument consisted of a primary questionnaire collecting history of BC and ovarian cancer (OC). This questionnaire was applied to women enrolled in the Emilia‐Romagna Breast Cancer Screening Program. General practitioners (GPs) and specialists could propose the same questionnaire too. Women with a score of ≥ 2, were invited to complete an oncogenetic counseling. According to the Tyrer‐Cuzick evaluation, women considered at high risk were invited to involve the most representative alive individual of the family affected with BC/OC for BRCA1/2 genetic testing. Results Since January 2012 and December 2016, 660 040 women were evaluated by the regional screening program, of which 22 289 (3.5%) were invited to the Spoke evaluation, but only 5615 accepted (25.2%). Totally, also considering women sent by GPs and specialists, 11 667 were assessed and 5554 were sent to the Hub evaluation. Finally, 2342 (42.8%) women fulfilled the criteria for genetic testing, and 544 (23.2%) resulted BRCA1/2 mutation carriers. Conclusions To our knowledge, this is the first regional population‐based multistep model that is aimed to identify individuals with BRCA1/2 mutations and to offer an intensive surveillance program for hereditary‐high risk women. This tool is feasible and effective, even if more efforts must be performed to increase the acceptance of multiple assessments by the study population., This is, to our knowledge, the first multistep approach, including regional breast cancer screening program, general practitioners and specialists, aimed to identify individuals with BRCA1/2 mutations and to offer an intensive surveillance program for hereditary, high and intermediate risk women. Our results show that a regional population‐based model is feasible, even if more efforts have to be yielded to increase the acceptance of subsequent assessments from women at risk.

Sentiment quantification is the task of estimating the relative frequency (or" prevalence") of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts; this is especially important when these texts are tweets, since most sentiment classification endeavours carried out on Twitter data actually have quantification (and not the classification of individual tweets) as their ultimate goal. It is well-known that solving quantification via" classify and count"(ie, by classifying all unlabelled items via a standard classifier and counting the items that have been assigned to a given class) is suboptimal in terms of accuracy, and that more accurate quantification methods exist. In 2016, Gao and Sebastiani carried out a systematic comparison of quantification methods on the task of tweet sentiment quantification. In hindsight, we observe that the experimental protocol followed in that work is flawed, and that its results are thus unreliable. We now re-evaluate those quantification methods on the very same datasets, this time following a now consolidated and much more robust experimental protocol, that involves 5775 as many experiments as run in the original study. Our experimentation yields results dramatically different from those obtained by Gao and Sebastiani, and thus provide a different, much more solid understanding of the relative strengths and weaknesses of different sentiment quantification methods.

Annual Report of the Artificial Intelligence for Media and Humanities laboratory (AIMH) research activities in 2020.

We present and make available MedLatin1 and MedLatin2, two datasets of medieval Latin texts to be used in research on computational authorship analysis. MedLatin1 and MedLatin2 consist of 294 and 30 curated texts, respectively, labelled by author, with MedLatin1 texts being of an epistolary nature and MedLatin2 texts consisting of literary comments and treatises about various subjects. As such, these two datasets lend themselves to supporting research in authorship analysis tasks, such as authorship attribution, authorship verification, or same-author verification.