Fabrizio Sebastiani

Alessandro Fabris

Andrea Esuli

Alejandro Moreo

This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.

Learning to Quantify

Fabrizio Silvestri

Roberto Grossi

This book constitutes the proceedings of the 18th International Symposium on String Processing and Information Retrieval, SPIRE 2011, held in Pisa, Italy, in October 2011. The 30 long and 10 short papers together with 1 keynote presented were carefully reviewed and selected from 102 submissions. The papers are structured in topical sections on introduction to web retrieval, sequence learning, computational geography, space-efficient data structures, algorithmic analysis of biological data, compression, text and algorithms.

18th International Symposium, SPIRE 2011, Pisa, Italy, October 17-21, 2011, Proceedings

String Processing and Information Retrieval

Djoerd Hiemstra

Josiane Mothe

Marie-Francine Moens

Raffaele Perego

Martin Potthast

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.

43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 – April 1, 2021, Proceedings, Part I

Advances in Information Retrieval

Fabrizio Sebastiani

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