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The problem of ranking documents in the Information Retrieval context has certain characteristics which make it difficult to define evaluation metrics: the decreasing probability of accessing low ranked documents and the unlimited size of document corpora. As a result we have a huge set of measures proposed in the state of the art. In order to shed some light on this issue, we will define and analyse a set of basic formal constraints that a desirable metric shoud satisfy. We will see that only the most recent metrics are able to satisfy every formal constraints.
In a second part, we will describe two complementary evaluation measures, Reliability and Sensitivity. These measures can be applied to any mixture of ranking, clustering and filtering tasks, satisfying more formal constraints than previously existing evaluation metrics for each of the sub-sumed tasks.
Lecturer: Professor Enrique Amigó
Enrique Amigó (UNED, Spain) has been assistant professor from 2005 at the Departamento de Lenguajes y Sistemas Informaticos. Member of the UNED group in Natural Language Processing and Information Retrieval. His current research interests include Textual Information Access and and evaluation methodologies. Relevant topics include document retrieval, topic detection, clustering, classification, summarization and translation. He published a wide set of refereed papers, most of them as first author. He has received an international award (Google). He had an active role in several international evaluation campaigns and research projects at regional, national, and European level.
Lecture: Axiometrics – Metrics for IR Evaluation and Generic Document Organization Tasks.
Marie-Francine Moens is a professor at the department of Computer Science of KU Leuven. She is head of the Language Intelligence and Information Retrieval group. She is author of more than 280 international peer reviewed publications and of several books. She is involved in the organization or program committee (as program chair, area chair or reviewer) of major conferences on computational linguistics, information retrieval and machine learning. She teaches the courses Text Based Information Retrieval and Natural Language Processing at KU Leuven in the Faculty of Engineering Science. She has given several invited tutorials in summer schools and international conferences and regularly gives keynotes at international conferences. She participates or has participated as partner or coordinator in numerous European and international projects. In 2011 and 2012 she was appointed as chair of the European Chapter of the Association for Computational Linguistics (EACL) and was a member of the executive board of the Association for Computational Linguistics (ACL). From 2010 until 2014 she was a member of the Research Council of KU Leuven and is currently a member of the Council of the Industrial Research Fund of this university. She is the scientific manager of the EU COST action iV&L (The European Network on Integrating Vision and Language). She is a member of the editorial board of the journal Foundations and Trends® in Information Retrieval. She was appointed as Scottish Informatics and Computer Science Alliance (SICSA) Distinguished Visiting Fellow in 2014.
Lecture: INTEGRATING SEMANTICS IN IR: ADVANCES IN LANGUAGE AND MULTIMEDIA PROCESSING
Dr. Vazirgiannis is a professor in LIX, Ecole Polytechnique. He holds a degree in Physics, a MSc. in Robotics, both from U. Athens (Greece), and a MSc. in Knowledge Based Systems from Heriot Watt University (Edinburgh, UK). He acquired a Ph.D. degree from the Dept. of Informatics, U. Athens, Greece. Since then, he has conducted research in GMD-IPSI (now Fraunhofer), Max Planck MPI (Germany) and INRIA (Paris). He has been a teaching at AUEB (Greece), Ecole Polytechnique, Telecom Paris, ENS (France), in Deusto University (Spain) and in Tsinghua (China).
His current research interests are on Graph/Text mining and recommendation algorithms. His has long experience with R&D projects in the areas: i. data mining and machine learning for large scale data repositories (i.e. the Web/social networks, medical data, time series etc.), ii. Data integration and pre-processing and exploration, iii. Web Mining and advertising/marketing. He recently established the Data Science and Mining team at the Informatics Laboratory in Ecole Polytechnique(France) focusing on Data Science and Machine Learning issues. He has supervised twelve completed PhD theses.
He has contributed chapters in books and encyclopedias, published two international books and more than a hundred thirty papers in international refereed journals and conference proceedings. He is actively involved in national and international research & development projects. He has received the ERCIM (2001) and the Marie Curie EU (2006) fellowships and held a DIGITEO Chair grant (2010-13) on Web Mining in France. He participates in the editorial boards of the Intelligent Data Analysis Journal while he served as guest editor for special issues of the “Machine Learning”, “Data Mining & Knowledge Discovery” and “IEEE Internet Computing” journals. He co-chaired the PC committee of ECML/PKDD 2011 conference, has served the Data Mining Track chair of the IEEE – ICDE 2011 conference and has participated as a conference committee member for more than fifty international conferences, in the areas: Data Bases, Data Mining/Machine learning and the Web. He is chairing the AXA “Data Science” chair in Ecole Polytechnique and has collaborations with the industry including Google and Airbus.
Human information interaction and retrieval (HIIR) blends research from information retrieval (IR), information behavior, and human computer interaction (HCI) to form a unique research specialty focused on helping people explore, resolve, and manage their information problems via interactions with information systems. Research in this area includes studies of people’s information search behaviors, their use of interfaces and search features, and their interactions with systems. One important characteristic of HIIR research is that it focuses on people; it is common for searchers to be studied along with their interactions with systems. Although many people would classify HIIR as a relatively new specialty because of the growth of online searching in the past two decades, interest in this area has existed since the early days of IR (and the first “user study” was conducted in the 1940s!). In this tutorial, I will provide a historical overview of human information interaction and retrieval research. I will present key ideas and concepts, the (d- )evolution of search interfaces, and methods of evaluation. The goal is to provide students with a greater understanding of, and appreciation for, HIIR studies, the kinds of research questions with which they are concerned, and the types of methods that are useful for addressing these questions.
Lecturer: Professor Diane Kelly
Diane Kelly is a Professor at the School of Information and Library Science at the University of North Carolina at Chapel Hill. Her research and teaching interests are in interactive information search and retrieval, information search behavior, and research methods. She is the recipient of the 2014 ASIST Research Award, the 2013 British Computer Society’s IRSG Karen Spärck Jones Award, the 2009 ASIST/Thomson Reuters Outstanding Information Science Teacher Award and the 2007 SILS Outstanding Teacher of the Year Award. She is the current ACM SIGIR treasurer and served as conference program committee co-chair in 2013. She serves on the editorial boards of several journals including ACM Transaction on Information Systems, Information Processing & Management, and Information Retrieval Journal. Kelly received a Ph.D., M.L.S. and graduate certificate in cognitive science from Rutgers University and a B.A. from the University of Alabama.
The Bag-of-words model has been the dominant approach for IR and Text mining for many years assuming the word independence and the frequencies as the main feature for feature selection and for query to document similarity. Although the long and successful usage, bag-of-words ignores words’ order and distance within the document – weakening thus the expressive power of the distance metrics. We propose graph-of-word, an alternative approach that capitalizes on a graph representation of documents and challenges the word independence assumption by taking into account words’ order and distance. We applied graph-of-word in various tasks such as ad-hoc Information Retrieval, Single-Document Keyword Extraction, Text Categorization and Sub-event Detection in Textual Streams. In all cases the the graph of word approach, assisted by degeneracy at times, outperforms the state of the art base lines in all cases.
Lecturer: PROFESSOR MICHALIS VAZIRGIANNIS
Katja Hofmann is a Researcher at the Machine Learning and Perception group at Microsoft Research in Cambridge (UK). Goal of her research is to develop technology that will allow online services, such as search engines, learn directly from interactions with their users. This would allow these services to go beyond learning the least common denominator for the user population as a whole, or for coarse user “buckets”, and would instead allow adaptation to individual users’ preferences and needs.
Before joining Microsoft Research, Katja obtained her PhD from the University of Amsterdam, where she worked with Maarten de Rijke and Shimon Whiteson as part of the ILPS group. Her PhD thesis brought together ideas from information retrieval and reinforcement learning, to advance IR towards “self-learning search engines”. She has published extensively at all major IR conferences as well as journals such as Information Retrieval and TOIS.
Machine Learning (ML) forms the basis of many Information Retrieval (IR) technologies, ranging from early work on text classification to recent approaches to entity linking, sentiment detection, and document ranking. In addition to serving as a key application area for ML, IR continuously pushes ML towards novel approaches.
In this talk I discuss and exemplify the dual role of IR as both a consumer of ML technology, and as a driver towards new challenging ML problems. I start with an overview of typical ML applications to IR, including an overview of learning to rank approaches. In the second part of the lecture I focus on a recent trend towards online learning approaches that allow continuous learning from user interactions. I discuss existing solutions, and conclude by highlighting open questions and directions for future research.
Lecturer: Dr. Katja Hofmann