Ewaluacja skuteczności systemów wyszukiwania informacji. Wyniki eksperymentu Polish Task realizowanego w ramach Conference and Labs of the Evaluation Forum (CLEF) 2012

W niniejszym artykule prezentujemy realizację laboratorium ewaluacyjnego CLEF (Conference and Labs of the Evaluation Forum) ze specjalnym uwzględnieniem kampanii CHiC (Cultural Heritage in CLEF). Opisujemy realizację oraz wyniki zadania Polish Task in ChiC. W artykule zaprezentowano wnioski z realzacji zadania. Zostały omówione wyniki uzyskane przez uczestników zadania przy użyciu różnych strategii indeksowania oraz wyszukiwania zasobów. Porównaliśmy efektywność metod tf-idf, OKAPI, DFR oraz data fusion.The article presents the design of CLEF (Conference and Labs of the Evaluation Forum) evaluation labs with special attention paid to CHiC (Cultural Heritage in CLEF). We describe design of Polish Task in CHiClab and discuss conclusions from lab realisation. We discuss results achieved by different participants using different indexing and matching approaches. Efficiency of tf-idf, OKAPI, DFR and data fusion was compared and analysed

Ewaluacja skuteczności systemów wyszukiwania informacji. Od eksperymentu Cranfield do laboratoriów TREC i CLEF. Geneza i metody

We present the genesis and evolution of methods and measures of IR systems evaluation. The design of the Cranfield experiment, a long-term model for evaluation methodology, is described. Evolution of current methodology of IR systems evaluation, developed at the annual TREC (Text REtrieval Conference) is provided, and the most popular and current measures described. The article presents also design of the CLEF (Conference and Labs of the Evaluation Forum) evaluation labs with special attention paid to CHiC (Cultural Heritage in CLEF). We describe the design of Polish Task in CHiClab and discuss conclusions from lab realisation.W niniejszym artykule prezentujemy rozwój metod i miar służących do oceny efektywności systemów informacyjno-wyszukiwawczych. Zostały w nim opisane założenia eksperymentu Cranfield, jako długoletniego wyznacznika metodologii ewaluacyjnej, oraz zarzuty stawiane organizacji samego eksperymentu. Ważną częścią artykułu jest także opis ewolucji powszechnie dziś stosowanej metodologii ewaluacji systemów informacyjno-wyszukiwawczych, wypracowanej podczas dorocznych konferencji TREC (Text REtrieval Conference), a także omówienie najpowszechniej obecnie stosowanych miar ewaluacyjnych w tym zakresie. Artykuł przedstawia również organizację laboratoriów ewaluacyjnych CLEF (Conference and Labs of the Evaluation Forum) ze szczególnym uwzględnieniem panelu CHiC (Cultural Heritage in CLEF), a na gruncie języka polskiego – Polish Task in CHiC

ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

This paper presents an overview of the ImageCLEF 2018 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) Labs 2018. ImageCLEF is an ongoing initiative (it started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval with the aim of providing information access to collections of images in various usage scenarios and domains. In 2018, the 16th edition of ImageCLEF ran three main tasks and a pilot task: (1) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based only on the figure image; (2) a tuberculosis task that aims at detecting the tuberculosis type, severity and drug resistance from CT (Computed Tomography) volumes of the lung; (3) a LifeLog task (videos, images and other sources) about daily activities understanding and moment retrieval, and (4) a pilot task on visual question answering where systems are tasked with answering medical questions. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks, shows an increasing interest in this benchmarking campaign

Evaluating Temporal Persistence Using Replicability Measures

In real-world Information Retrieval (IR) experiments, the Evaluation Environment (EE) is exposed to constant change. Documents are added, removed, or updated, and the information need and the search behavior of users is evolving. Simultaneously, IR systems are expected to retain a consistent quality. The LongEval Lab seeks to investigate the longitudinal persistence of IR systems, and in this work, we describe our participation. We submitted runs of five advanced retrieval systems, namely a Reciprocal Rank Fusion (RRF) approach, ColBERT, monoT5, Doc2Query, and E5, to both sub-tasks. Further, we cast the longitudinal evaluation as a replicability study to better understand the temporal change observed. As a result, we quantify the persistence of the submitted runs and see great potential in this evaluation method.Comment: To be published in Proceedings of the Working Notes of CLEF 2023 - Conference and Labs of the Evaluation Forum, Thessaloniki, Greece 18 - 21, 202

TOBB-ETU at CLEF 2019: Prioritizing claims based on check-worthiness

20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF ( 2019: Lugano; Switzerland)In recent years, we witnessed an incredible amount of misinformation spread over the Internet. However, it is extremely time consuming to analyze the veracity of every claim made on the Internet. Thus, we urgently need automated systems that can prioritize claims based on their check-worthiness, helping fact-checkers to focus on important claims. In this paper, we present our hybrid approach which combines rule-based and supervised methods for CLEF-2019 Check That! Lab's Check-Worthiness task. Our primary model ranked 9th based on MAP, and 6th based on R-P, P@5, and P@20 metrics in the official evaluation of primary submissions. © 2019 CEUR-WS. All rights reserved

Overview of ImageCLEF 2017: Information extraction from images

This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2017. ImageCLEF is an ongoing initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. In 2017, the 15th edition of ImageCLEF, three main tasks were proposed and one pilot task: (1) a LifeLog task about searching in LifeLog data, so videos, images and other sources; (2) a caption prediction task that aims at predicting the caption of a figure from the biomedical literature based on the figure alone; (3) a tuberculosis task that aims at detecting the tuberculosis type from CT (Computed Tomography) volumes of the lung and also the drug resistance of the tuberculosis; and (4) a remote sensing pilot task that aims at predicting population density based on satellite images. The strong participation of over 150 research groups registering for the four tasks and 27 groups submitting results shows the interest in this benchmarking campaign despite the fact that all four tasks were new and had to create their own community

ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020

Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in Tweets

We present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the dataset and the task setup, including the evaluation settings, and we give a brief overview of the participating systems. As usual in the CheckThat! lab, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research on finding relevant tweets that can help different stakeholders such as fact-checkers, journalists, and policymakers