CLEF promotes the systematic evaluation of information access systems, primarily through experimentation on shared tasks. Labs are of two types: laboratories to conduct evaluation of information access systems, and workshops to discuss and pilot innovative evaluation activities. There are eight labs for CLEF 2014, listed below.

CLEF2014 CEUR-WS CEUR-WS Lab Proceedings for CLEF 2014 now available online

NEWSREEL — News Recommendation Evaluation Lab

NEWSREEL offers two tasks:
  • Task 1: Predict the items a user will click in the next 10 Minutes based on the offline dataset. A sliding window approach is used for the evaluation of the recommender algorithm’s quality. The main emphasis is here on the reproducible, deep analysis of the user’s behavior.
  • Task 2: Predict the articles users will click. The prediction algorithms are evaluated in an online scenario based on live user-interactions. The main focus is on providing real-time recommendations for current news articles. Participants will be allowed to fine-tune their algorithms before submission starts in the weeks leading up to CLEF 2014.
Lab Coordination: Technische Universität Berlin, plista GmbH, Berlin.
Lab website:

CLEF eHealth - ShARe/CLEF eHealth Evaluation Lab

The usage scenario of the CLEF eHealth lab is to ease patients and next-of-kins’ ease in understanding eHealth information. The lab contains three tasks:
  • Visual-Interactive Search and Exploration of eHealth Data
  • Information extraction from clinical text
  • User-centred health information retrieval
Lab Coordination: Dublin City University; Universities of Arizone, Konstanz, Canberra, Utah, Pittsburgh, Melbourne, Turku; Australian National University; SICS and Stockholm University; NICTA; DSV Stockholm University; Columbia University; KTH and Gavagai; Karolinska Institutet; Harvard Medical School and Boston Children's Hospital; Vienna University of Technology; HES-SO; Charles University; and the Australian e-Health Research Centre.
Lab website:

QA Track — CLEF Question Answering Track

In the current general scenario for the CLEF QA Track, the starting point is always a Natural Language question. However, answering some questions may need to query Linked Data (especially if aggregations or logical inferences are required); whereas some questions may need textual inferences and querying free-text. Answering some queries may need both. The tasks are:
  • QALD: Question Answering over Linked Data
  • BioASQ: Biomedical semantic indexing and question answering
  • Entrance Exams
Lab Coordination: INRIA, NCSR, Carnegie Mellon, University Leipzig, UNED, University of Limerick, CITEC
Lab website:


ImageCLEF aims at providing benchmarks for the challenging task of image annotation for a wide range of source images and annotation objective, such as general multi-domain images for object or concept detection, as well as domain-specific tasks such as visual-depth images for robot vision and volumetric medical images for automated structured reporting. The tasks address different aspects of the annotation problem and are aimed at supporting and promoting the cutting-edge research addressing the key challenges in the field, such as multi-modal image annotation, domain adaptation and ontology driven image annotation. The Lab tasks are:
  • Robot Vision
  • Scalable concept Image Annotation
  • Liver CT Annotation
  • Domain Adaptation
Lab Coordination: University of Rome La Sapienza, University of Castila-La Mancha.
Lab website:

PAN Lab on Uncovering Plagiarism, Au-thorship, and Social Software Misuse

PAN centers around the topics of plagiarism, authorship, and social software misuse. The goal is to foster research on automatic detection and uncovering. People increas-ingly share their work online, contribute to open projects and engage in web-based social interactions. The ease and anonymity with which this can be done raises concerns about verifiability and trust: Is a given text an original? Is the author the one who she claims to be? Does a piece of information come from a trusted source? Answers to such questions are crucial to deal with and to rely on infor-mation obtained online, while the scale at which answers should be given calls for an automatic means. Lab Tasks:
  • Author Identification
  • Author Profiling
  • Plagiarism Detection
Lab Coordination: Bauhaus-Universität Weimar, Universitat Politècnica de València, University of the Aegean
Lab website:

INEX — Initiative for the Evaluation of XML retrieval

INEX builds evaluation benchmarks for search in the context of rich structure such as document structure, semantic metadata, entities, or genre/topical structure. INEX 2014 runs four tasks studying different aspects of focused information access:
  • Social Book Search Task: investigates the relative value of authoritative metadata and user-generated content. The test collection is from Amazon and LibraryThing, and user profiles and personal catalogues.
  • Interactive Social Book Search Task: investigates user information seeking behavior when interacting with various sources of information for realistic task scenarios, and how the user interface impacts search and the search experience.
  • Linked Data Task: investigates complex questions to be answered by DBpedia/Wikipedia, with the help of SPARQL queries and additional keyword filters, aiming to express natural language search cues more effectively (in collaboration with the QA Lab).
  • Tweet Contextualization Task: investigates tweet contextualization, helping a user understand a tweet by providing a short background summary generated from relevant Wikipedia passages aggregated into a coherent summary (in collaboration with RepLab).
Lab Coordination: Queensland University of Technology, University of Amsterdam, University of Passau.
Lab website:


The aim of RepLab is to bring together the Information Access research community with representatives from the Online Reputation Management industry, with the ultimate goals of (i) establishing a roadmap on the topic that includes a description of the language technologies required in terms of resources, algorithms, and applications; (ii) specifying suitable evaluation methodologies and metrics to measure scientific progress; and (iii) developing of test collections that enable systematic comparison of algorithms and reliable benchmarking of commercial systems. Lab Tasks:
  • Task 1. Reputation Dimensions
  • Task 2. Author Profiling
Lab Coordination: UNED, University of Amsterdam, Yahoo! Research Barcelona, Llorente & Cuenca
Lab website:


LifeCLEF aims at evaluating multimedia analysis and retrieval techniques on biodiversity data for species indentification. Tasks are:
  • BirdCLEF: a bird songs identification task based on Xeno-Canto audio recordings
  • PlantCLEF: an image-based plant identification task based on the data of Tela Botanica social network
  • FishCLEF: a fish video surveillance task based on the data of thr Fish4Knowledge network
Lab Coordination: INRIA Sophia-Antipolis, University of Applied Sciences Western Switzerland, University of Toulon, INRIA, TU Wien, Xeno-Canto, Cirad – AMAP, University of Catania, University of Edinburgh
Lab webpage: