Table of Contents

• Task 1 of the CLEF eHealth Evaluation Lab 2016: Handover Information ExtractionHanna Suominen, Liyuan Zhou, Lorraine Goeuriot, Liadh Kelly. 1-14 [doi]
• The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information RetrievalGuido Zuccon, João R. M. Palotti, Lorraine Goeuriot, Liadh Kelly, Mihai Lupu, Pavel Pecina, Henning Müller, Julie Budaher, Anthony Deacon. 15-27 [doi]
• Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016Aurélie Névéol, Kevin Bretonnel Cohen, Cyril Grouin, Thierry Hamon, Thomas Lavergne, Liadh Kelly, Lorraine Goeuriot, Grégoire Rey, Aude Robert, Xavier Tannier, Pierre Zweigenbaum. 28-42 [doi]
• Comparison of Several Word embedding Sources for Medical Information RetrievalJulie Budaher, Mohannad Almasri, Lorraine Goeuriot. 43-46 [doi]
• SIBM at CLEF eHealth Evaluation Lab 2016: Extracting Concepts in French Medical Texts with ECMT and CIMINDChloé Cabot, Lina Fatima Soualmia, Badisse Dahamna, Stéfan Jacques Darmoni. 47-60 [doi]
• ECSTRA-INSERM @ CLEF eHealth2016-task 2: ICD10 Code Extraction from Death CertificatesMohamed Dermouche, Vincent Looten, Rémi Flicoteaux, Sylvie Chevret, Julien Velcin, Namik Taright. 61-68 [doi]
• Wrappers for Feature Subset Selection in CRF-based Clinical Information ExtractionMike Ebersbach, Robert Herms, Christina Lohr, Maximilian Eibl. 69-80 [doi]
• LITL at CLEF eHealth2016: recognizing Entities in French Biomedical DocumentsLydia-Mai Ho-Dac, Ludovic Tanguy, Céline Grauby, Nkauj Hnub Aurore Heu Mby, Justine Malosse, Laura Rivière, Amélie Veltz-Mauclair, Marine Wauquier. 81-93 [doi]
• BiTeM at CLEF eHealth Evaluation Lab 2016 Task 2: Multilingual Information ExtractionLuc Mottin, Julien Gobeill, Anaïs Mottaz, Emilie Pasche, Arnaud Gaudinat, Patrick Ruch. 94-102 [doi]
• KISTI at CLEF eHealth 2016 Task 3: Ranking Medical Documents using Word VectorsHeung-Seon Oh, Yuchul Jung. 103-108 [doi]
• Distributional Semantics for Medical Information ExtractionLautaro Quiroz, Lydia Mennes, Mostafa Dehghani, Evangelos Kanoulas. 109-122 [doi]
• Task3 Patient-Centred Information Retrieval: Team CUNIShadi Saleh, Pavel Pecina. 123-129 [doi]
• The Effectiveness of Query Expansion when searching for Health related Content: InfoLab at CLEF eHealth 2016Ricardo Silva, Carla Lopes. 130-142 [doi]
• Team GU-IRLAB at CLEF eHealth 2016: Task 3Luca Soldaini, Will Edman, Nazli Goharian. 143-146 [doi]
• ECNU at 2016 eHealth Task 1: Handover Information ExtractionYang Song, Yun He, Hongyu Liu, Qinmin Hu, Liang He, Yueyao Wang. 147-156 [doi]
• ECNU at 2016 eHealth Task 3: Patient-centred Information RetrievalYang Song, Yun He, Hongyu Liu, Yueyao Wang, Qinmin Hu, Liang He. 157-161 [doi]
• Task 3: Patient-Centered Information Retrieval, IRTask 1: ad-hoc search - TEAM ub-botswanaEdwin Thuma, Nkwebi Peace Motlogelwa, Tebo Leburu-Dingalo. 162-166 [doi]
• KDEIR at CLEF eHealth 2016: Health Documents Re-ranking Based on Query VariationsMd Zia Ullah, Masaki Aono. 167-170 [doi]
• Erasmus MC at CLEF eHealth 2016: Concept Recognition and Coding in French TextsErik M. van Mulligen, Zubair Afzal, Saber A. Akhondi, Dang Vo, Jan A. Kors. 171-178 [doi]
• Semantic Tagging and Normalization of French Medical EntitiesJorge Vivaldi, Horacio Rodríguez, Viviana Cotik. 179-192 [doi]
• WHUIRGroup at the CLEF 2016 eHealth Lab Task 3Ruixue Wang, Wei Lu, Ke Ren. 193-197 [doi]
• MayoNLPTeam at the 2016 CLEF eHealth Information Retrieval Task 1Yanshan Wang, Stephen Tze-Inn Wu, Hongfang Liu. 198-204 [doi]
• LIMSI ICD10 coding Experiments on CépiDC Death Certificate StatementsPierre Zweigenbaum, Thomas Lavergne. 205-218 [doi]
• Overview of the ImageCLEF 2016 Medical TaskAlba Garcia Seco de Herrera, Roger Schaer, Stefano Bromuri, Henning Müller. 219-232 [doi]
• Overview of the ImageCLEF 2016 Handwritten Scanned Document Retrieval TaskMauricio Villegas, Joan Puigcerver, Alejandro Héctor Toselli, Joan-Andreu Sánchez, Enrique Vidal. 233-253 [doi]
• Overview of the ImageCLEF 2016 Scalable Concept Image Annotation TaskAndrew Gilbert, Luca Piras, Josiah Wang, Fei Yan, Arnau Ramisa, Emmanuel Dellandréa, Robert J. Gaizauskas, Mauricio Villegas, Krystian Mikolajczyk. 254-278 [doi]
• DUTh at the ImageCLEF 2016 Image Annotation Task: Content SelectionGeorgios Barlas, Maria Ntonti, Avi Arampatzis. 279-287 [doi]
• Using Machine Learning Techniques, Textual and Visual Processing in Scalable Concept Image Annotation ChallengeAlexandru Cristea, Adrian Iftene. 288-298 [doi]
• UAEMex at ImageCLEF 2016: Handwritten RetrievalMiguel Ángel García Calderón, René Arnulfo García-Hernández, Yulia Ledeneva. 299-303 [doi]
• Traditional Feature Engineering and Deep Learning Approaches at Medical Classification Task of ImageCLEF 2016Sven Koitka, Christoph M. Friedrich. 304-317 [doi]
• Subfigure and Multi-Label Classification using a Fine-Tuned Convolutional Neural NetworkAshnil Kumar, David Lyndon, Jinman Kim, David Feng. 318-321 [doi]
• Image Annotation and Two Paths to Text IllustrationHervé Le Borgne, Etienne Gadeski, Ines Chami, Thi Quynh Nhi Tran, Youssef Tamaazousti, Alexandru-Lucian Gînsca, Adrian Popescu 0001. 322-333 [doi]
• UDEL CIS Working Notes in ImageCLEF 2016Pengyuan Li, Scott Sorensen, Abhishek Kolagunda, Xiangying Jiang, Xiaolong Wang, Chandra Kambhamettu, Hagit Shatkay. 334-346 [doi]
• MayoBMI at ImageCLEF 2016 Handwritten Document Retrieval TaskSijia Liu, Yanshan Wang, Saeed Mehrabi, Dingcheng Li, Hongfang Liu. 347-355 [doi]
• INAOE's participation at ImageCLEF 2016: Text Illustration TaskLuis Pellegrin, Adrián Pastor López-Monroy, Hugo Jair Escalante, Manuel Montes-y-Gómez. 356-362 [doi]
• MRIM-LIG at ImageCLEF 2016 Scalable Concept Image Annotation TaskMaxime Portaz, Mateusz Budnik, Philippe Mulhem, Johann Poignant. 363-370 [doi]
• CNRS TELECOM ParisTech at ImageCLEF 2016 Scalable Concept Image Annotation Task: Overcoming the Scarcity of Training DataHichem Sahbi. 371-385 [doi]
• NovaSearch at ImageCLEFmed 2016 Subfigure Classification TaskDavid Semedo, João Magalhães. 386-398 [doi]
• CITlab ARGUS for Keyword Search in Historical Handwritten Documents - Description of CITlab's System for the ImageCLEF 2016 Handwritten Scanned Document Retrieval TaskTobias Strauß, Tobias Grüning, Gundram Leifert, Roger Labahn. 399-412 [doi]
• IPL at CLEF 2016 Medical TaskLeonidas Valavanis, Spyridon Stathopoulos, Theodore Kalamboukis. 413-420 [doi]
• Joint Learning of CNN and LSTM for Image CaptioningYongqing Zhu, Xiangyang Li, Xue Li, Jian Sun, Xinhang Song, Shuqiang Jiang. 421-427 [doi]
• Plant Identification in an Open-world (LifeCLEF 2016)Hervé Goëau, Pierre Bonnet, Alexis Joly. 428-439 [doi]
• LifeCLEF Bird Identification Task 2016: The arrival of Deep learningHervé Goëau, Hervé Glotin, Willem-Pier Vellinga, Robert Planquè, Alexis Joly. 440-449 [doi]
• Floristic participation at LifeCLEF 2016 Plant Identification TaskJulien Champ, Hervé Goëau, Alexis Joly. 450-458 [doi]
• Bluefield (KDE TUT) at LifeCLEF 2016 Plant Identification TaskSiang Thye Hang, Atsushi Tatsuma, Masaki Aono. 459-468 [doi]
• Unsupervised Individual Whales Identification: Spot the Difference in the OceanAlexis Joly, Jean-Christophe Lombardo, Julien Champ, Anjara Saloma. 469-480 [doi]
• SeaCLEF 2016: Object Proposal Classification for Fish Detection in Underwater VideosJonas Jäger, Erik Rodner, Joachim Denzler, Viviane Wolff, Klaus Fricke-Neuderth. 481-489 [doi]
• Improving Bird Identification using Multiresolution Template Matching and Feature Selection during TrainingMario Lasseck. 490-501 [doi]
• Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification TaskSue Han Lee, Yang Loong Chang, Chee Seng Chan, Paolo Remagnino. 502-510 [doi]
• Feature Learning via Mixtures of DCNNs for Fine-Grained Plant ClassificationChris McCool, ZongYuan Ge, Peter I. Corke. 511-517 [doi]
• Open-set Plant Identification Using an Ensemble of Deep Convolutional Neural NetworksMostafa Mehdipour-Ghazi, Berrin A. Yanikoglu, Erchan Aptoula. 518-524 [doi]
• Object Detection, Classification, Tracking and individual Recognition for Sea Images and VideosDávid Papp, Dániel Lovas, Gábor Szücs. 525-533 [doi]
• Recognizing Bird Species in Audio Recordings using Deep Convolutional Neural NetworksKarol J. Piczak. 534-543 [doi]
• Bag of MFCC-based Words for Bird IdentificationJulien Ricard, Hervé Glotin. 544-546 [doi]
• Audio Based Bird Species Identification using Deep Learning TechniquesElias Sprengel, Martin Jaggi, Yannic Kilcher, Thomas Hofmann. 547-559 [doi]
• Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy EnvironmentBálint Pál Tóth, Bálint Czeba. 560-568 [doi]
• Deep Learning and SVM Classification for Plant Recognition in Content-Based Large Scale Image RetrievalBálint Pál Tóth, Márton Tóth, Dávid Papp, Gábor Szücs. 569-578 [doi]
• Very Deep Residual Networks with MaxOut for Plant Identification in the WildMilan Sulc, Dmytro Mishkin, Jiri Matas. 579-586 [doi]
• Living Ranking: from Online to Real-time Information Retrieval EvaluationLamjed Ben Jabeur, Laure Soulier, Paul Mousset, Lynda Tamine. 587-588 [doi]
• Research-Centres Centred Living LabsLiadh Kelly. 589-590 [doi]
• Ideas for a Standard LL4IR Extension - Living Labs from a System Operator's PerspectivePhilipp Schaer, Narges Tavakolpoursaleh. 591-592 [doi]
• CLEF NewsREEL 2016: Comparing Multi-dimensional Offline and Online Evaluation of News Recommender SystemsBenjamin Kille, Andreas Lommatzsch, Frank Hopfgartner, Martha Larson, Jonas Seiler, Davide Malagoli, András Serény, Torben Brodt. 593-605 [doi]
• Development of a News Recommender System based on Apache FlinkAlexandru Ciobanu, Andreas Lommatzsch. 606-617 [doi]
• CLEF NewsREEL 2016: Image based RecommendationFrancesco Corsini, Martha Larson. 618-827 [doi]
• Real-time News Recommendations using Apache SparkJaschar Domann, Jens Meiners, Lea Helmers, Andreas Lommatzsch. 628-641 [doi]
• Recommender Systems Evaluations : Offline, Online, Time and A/A TestGebrekirstos G. Gebremeskel, Arjen P. de Vries. 642-656 [doi]
• Recommender Ensembles for News Articles based on Most-Popular StrategiesAndreas Lommatzsch, Niels Johannes, Jens Meiners, Lea Helmers, Jaschar Domann. 657-668 [doi]
• Optimizing a Scalable News Recommender SystemPatrick Probst, Andreas Lommatzsch. 669-678 [doi]
• Clicks Pattern Analysis for Online News Recommendation SystemsJing Yuan, Andreas Lommatzsch, Benjamin Kille. 679-690 [doi]
• Clustering by Authorship Within and Across DocumentsEfstathios Stamatatos, Michael Tschuggnall, Ben Verhoeven, Walter Daelemans, Günther Specht, Benno Stein, Martin Potthast. 691-715 [doi]
• Author Obfuscation: Attacking the State of the Art in Authorship VerificationMartin Potthast, Matthias Hagen, Benno Stein. 716-749 [doi]
• Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre EvaluationsFrancisco Manuel Rangel Pardo, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Potthast, Benno Stein. 750-784 [doi]
• Age and Gender Identification using Stacking for ClassificationMadhulika Agrawal, Teresa Gonçalves. 785-790 [doi]
• Authorship Clustering using Multi-headed Recurrent Neural NetworksDouglas Bagnall. 791-804 [doi]
• Author Profiling Using Support Vector MachinesRodwan Bakkar Deyab, José Duarte, Teresa Gonçalves. 805-814 [doi]
• Author Profiling using SVMs and Word Embedding AveragesRoy Khristopher Bayot, Teresa Gonçalves. 815-823 [doi]
• CAPS: A Cross-genre Author Profiling SystemIvan Bilan, Desislava Zhekova. 824-835 [doi]
• Author Profiling using Complementary Second Order Attributes and Stylometric FeaturesKonstantinos Bougiatiotis, Anastasia Krithara. 836-845 [doi]
• GronUP: Groningen User ProfilingMart Busger Op Vollenbroek, Talvany Carlotto, Tim Kreutz, Maria Medvedeva, Chris Pool, Johannes Bjerva, Hessel Haagsma, Malvina Nissim. 846-857 [doi]
• Using Machine Learning Algorithms for Author Profiling In Social MediaDaniel Dichiu, Irina Rancea. 858-863 [doi]
• Profile-based Approach for Age and Gender IdentificationMaria José Garciarena Ucelay, Maria Paula Villegas, Dario G. Funez, Leticia C. Cagnina, Marcelo Luis Errecalde, Gabriela Ramírez-de-la-Rosa, Esaú Villatoro-Tello. 864-873 [doi]
• PANcakes Team: A Composite System of Domain-Agnostic Features For Author ProfilingPepa Gencheva, Martin Boyanov, Elena Deneva, Preslav Nakov, Georgi Georgiev, Yasen Kiprov, Ivan Koychev. 874-880 [doi]
• Statistical Semantics in Context Space : Amrita_CEN@Author ProfilingBarathi Ganesh H. B., M. Anand Kumar, K. P. Soman. 881-889 [doi]
• Author Masking through TranslationYashwant Keswani, Harsh Trivedi, Parth Mehta 0001, Prasenjit Majumder. 890-894 [doi]
• UniNE at CLEF 2016: Author ClusteringMirco Kocher. 895-902 [doi]
• UniNE at CLEF 2016: Author ProfilingMirco Kocher, Jacques Savoy. 903-911 [doi]
• Methods for Intrinsic Plagiarism Detection and Author DiarizationMikhail Kuznetsov, Anastasia Motrenko, Rita Kuznetsova, Vadim Strijov. 912-919 [doi]
• Evaluating Safety, Soundness and Sensibleness of Obfuscation SystemsMatthias Liebeck, Pashutan Modaresi, Stefan Conrad 0001. 920-928 [doi]
• Derivative Approach for Plagiarism Source RetrievalRhulani Maluleka. 929-931 [doi]
• Multi Feature Space Combination for Authorship ClusteringMuharram Mansoorizadeh, Mohammad Aminian, Taher Rahgooy, Mehdy Eskandari. 932-938 [doi]
• Author Obfuscation using WordNet and Language ModelsMuharram Mansoorizadeh, Taher Rahgooy, Mohammad Aminian, Mehdy Eskandari. 939-946 [doi]
• Adapting Cross-Genre Author Profiling to Language and CorpusIlia Markov, Helena Gómez-Adorno, Grigori Sidorov, Alexander F. Gelbukh. 947-955 [doi]
• SU@PAN'2016: Author ObfuscationTsvetomila Mihaylova, Georgi Karadjov, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov. 956-969 [doi]
• Exploring the Effects of Cross-Genre Machine Learning for Author Profiling in PAN 2016Pashutan Modaresi, Matthias Liebeck, Stefan Conrad 0001. 970-977 [doi]
• Profiling Microblog Authors using Concreteness and Sentiment - Know-Center at PAN 2016 Author ProfilingOliver Pimas, Andi Rexha, Mark Kröll, Roman Kern. 978-983 [doi]
• Exploring Word Embeddings and Character N-Grams for Author ClusteringYunita Sari, Mark Stevenson. 984-991 [doi]
• Cross-Genre Author Profile Prediction Using Stylometry-Based ApproachShaina Ashraf, Hafiz Rizwan Iqbal, Rao Muhammad Adeel Nawab. 992-999 [doi]
• Author Diarization Using Cluster-Distance ApproachAbdul Sittar, Hafiz Rizwan Iqbal, Rao Muhammad Adeel Nawab. 1000-1007 [doi]
• A Big Increase in Known Unknowns: from Author Verification to Author ClusteringAnna Vartapetiance, Lee Gillam. 1008-1013 [doi]
• Cross-Genre Age and Gender Identification in Social MediaAnam Zahid, Aadarsh Sampath, Anindya Dey, Golnoosh Farnadi. 1014-1017 [doi]
• Experiments in Authorship-Link Ranking and Complete Author ClusteringValentin Zmiycharov, Dimitar Alexandrov, Hristo Georgiev, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov. 1018-1023 [doi]
• Overview of the SBS 2016 Interactive TrackMaria Gäde, Mark Michael Hall, Hugo C. Huurdeman, Jaap Kamps, Marijn Koolen, Mette Skov, Toine Bogers, David Walsh. 1024-1038 [doi]
• Overview of the SBS 2016 Suggestion TrackMarijn Koolen, Toine Bogers, Jaap Kamps. 1039-1052 [doi]
• Overview of the SBS 2016 Mining TrackToine Bogers, Iris Hendrickx, Marijn Koolen, Suzan Verberne. 1053-1063 [doi]
• Linking Task: Identifying Authors and Book Titles in Verbose QueriesAnaïs Ollagnier, Sébastien Fournier, Patrice Bellot. 1064-1071 [doi]
• Verbose Query Reduction by Learning to Rank for Social Book Search TrackMessaoud Chaa, Omar Nouali, Patrice Bellot. 1072-1078 [doi]
• SBS 2016 Track Mining: Classification with Linguistic Features for Book Search Requests ClassificationMohamed Ettaleb, Chiraz Latiri, Brahim Douar, Patrice Bellot. 1079-1088 [doi]
• USTB at Social Book Search 2016 Suggestion Task: Active Books Set and Re-rankingShao-Hui Feng, Bo-Wen Zhang, Xu-Cheng Yin, Zan-Xia Jin, Jian-Lin Jin, Jian-wei Wu, Le-Le Zhang, Hao-Jie Pan, Fan Fang, Fang Zhou. 1089-1096 [doi]
• Hood or Hypertext: A Comparison of Offline and Online Book Search SessionsMaria Gäde, Vivien Petras. 1097-1105 [doi]
• Individual Differences and Task BehaviourMark Michael Hall, Marijn Koolen. 1106-1114 [doi]
• SBS 2016 : Combining Query Expansion Result and Books Information Score for Book RecommendationAmal Htait, Sébastien Fournier, Patrice Bellot. 1115-1122 [doi]
• BM25 for Non-Textual Modalities in Social Book SearchMelanie Imhof. 1123-1129 [doi]
• Social Book Search Track: ISM@CLEF'16 Suggestion TaskRitesh Kumar, Bhanodai Guggilla, Rajendra Pamula. 1130-1135 [doi]
• Word Embedding for Social Book SuggestionNawal Ould Amer, Philippe Mulhem, Mathias Géry, Karam Abdulahhad. 1136-1144 [doi]
• OAUC at CLEF2016 SBS Lab: Using Appeal Elements to Improve Automatic Book Recommendation - Proof of ConceptMichael Preminger, Gjertrud Fludal. 1145-1154 [doi]
• Query Expansion by Word Embedding in the Suggestion Track of CLEF 2016 Social Book Search LabShih-Hung Wu, Yi-Hsiang Hsieh, Liang-Pu Chen, Ping-Che Yang. 1155-1165 [doi]
• I Need More Time!: The Influence of Native Language on Search Behavior and ExperiencePengyi Zhang, Chang Liu, Preben Hansen. 1166-1182 [doi]
• KNOW At The Social Book Search Lab 2016 Suggestion TrackHermann Ziak, Roman Kern. 1183-1189 [doi]
• KNOW At The Social Book Search Lab 2016 Mining TrackHermann Ziak, Andi Rexha, Roman Kern. 1190-1196 [doi]
• Cultural micro-blog Contextualization 2016 Workshop Overview: data and pilot tasksLiana Ermakova, Lorraine Goeuriot, Josiane Mothe, Philippe Mulhem, Jian-Yun Nie, Eric SanJuan. 1197-1200 [doi]
• LIG at CLEF 2016 Cultural Microblog Contextualization: TimeLine illustration based on MicroblogsNayanika Dogra, Philippe Mulhem, Nawal Ould Amer, Lorraine Goeuriot. 1201-1206 [doi]
• Semantic Mapping: Towards Contextual and Trend Analysis of Behaviours and PracticesFionn Murtagh. 1207-1225 [doi]
• Building a Knowledge Base using Microblogs: the Case of Cultural MicroBlog Contextualization CollectionHoang Thi Bich Ngoc, Josiane Mothe. 1226-1237 [doi]
• Tweet Contextualization using Continuous Space Vectors: Automatic Summarization of Cultural DocumentsElvys Linhares Pontes, Juan Manuel Torres Moreno, Stéphane Huet, Andréa Carneiro Linhares. 1238-1245 [doi]
• Tweet Data mining : the Cultural Microblog Contextualization Data SetClémentine Scohy, Yassine Rkha Chaham, Sébastien Déjean, Josiane Mothe. 1246-1259 [doi]