CLEF-WN 2021
CLEF 2021 Working Notes

Table of Contents

• Preface
  Guglielmo Faggioli, Nicola Ferro, Alexis Joly, Maria Maistro, Florina Piroi
  

ARQMath: Answer Retrieval for Questions on Math

• Overview of ARQMath-2 (2021): Second CLEF Lab on Answer Retrieval for Questions on Math (Working Notes Version)1-24
  Behrooz Mansouri, Richard Zanibbi, Douglas W. Oard, Anurag Agarwal
  
• XY-PHOC Symbol Location Embeddings for Math Formula Retrieval and Autocompletion25-35
  Robin Avenoso, Behrooz Mansouri, Richard Zanibbi
  
• BERT-Based Embedding Model for Formula Retrieval36-46
  Pankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay
  
• DPRL Systems in the CLEF 2021 ARQMath Lab: Sentence-BERT for Answer Retrieval, Learning-to-Rank for Formula Retrieval47-62
  Behrooz Mansouri, Douglas W. Oard, Richard Zanibbi
  
• Dowsing for Answers to Math Questions: Ongoing Viability of Traditional MathIR63-81
  Yin Ki Ng, Dallas Fraser, Besat Kassaie, Frank Tompa
  
• Ensembling Math Information Retrieval Systems: MIRMU and MSM at ARQMath 202182-106
  Vít Novotný, Michal Štefánik, Dávid Lupták, Martin Geletka, Petr Zelina, Petr Sojka
  
• TU_DBS in the ARQMath Lab 2021, CLEF107-124
  Anja Reusch, Maik Thiele, Wolfgang Lehner
  
• Ranked List Fusion and Re-ranking with Pre-trained Transformers for ARQMath Lab125-132
  Shaurya Rohatgi, Jian Wu, C Lee Giles
  
• Approach Zero and Anserini at the CLEF-2021 ARQMath Track: Applying Substructure Search and BM25 on Operator Tree Path Tokens133-156
  Wei Zhong, Xinyu Zhang, Ji Xin, Richard Zanibbi, Jimmy Lin
  

BioASQ: Large-scale biomedical semantic indexing and question answering

• Overview of BioASQ Tasks 9a, 9b and Synergy in CLEF2021157-164
  Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Georgios Paliouras
  
• Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials.165-187
  Luis Gasco, Anastasios Nentidis, Anastasia Krithara, Darryl Estrada-Zavala, Renato Toshiyuki Murasaki, Elena Primo-Peña, Cristina Bojo-Canales, Georgios Paliouras, Martin Krallinger
  
• BioASQ Synergy: A strong and simple baseline rooted in relevance feedback188-195
  Tiago Almeida, Sérgio Matos
  
• Universal Passage Weighting Mecanism (UPWM) in BioASQ 9b196-212
  Tiago Almeida, Sérgio Matos
  
• Large Biomedical Question Answering Models with ALBERT and ELECTRA213-220
  Sultan Alrowili, Vijay Shanker
  
• Vicomtech at MESINESP2: BERT-based Multi-label Classification Models for Biomedical Text Indexing221-230
  Aitor García-Pablos, Naiara Perez, Montse Cuadros
  
• MDS_UNCC Question Answering System for Biomedical Data with Preliminary Error Analysis231-240
  Seethalakshmi Gopalakrishnan, Swathi Padithala, Hilmi Demirhan, Wlodek Zadrozny
  
• PIDNA at BioASQ MESINESP: Hybrid Semantic Indexing for Biomedical Articles in Spanish241-246
  Yi Huang, Buse Giledereli, Abdullatif Koksal, Arzucan Ozgur, Elif Ozkirimli
  
• Transformer-based Language Models for Factoid Question Answering at BioASQ9b247-257
  Urvashi Khanna, Diego Molla
  
• Post-processing BioBERT And Using Voting Methods for Biomedical Question Answering258-273
  Margarida M. Campos, Francisco M. Couto
  
• Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions274-285
  Diego Molla, Urvashi Khanna, Dima Galat, Vincent Nguyen, Maciej Rybinski
  
• End-to-end Biomedical Question Answering via Bio-AnswerFinder and Discriminative Language Representation Models286-301
  Ibrahim Burak Ozyurt
  
• A Neural Text Ranking Approach for Automatic MeSH Indexing302-312
  Alastair Rae, James Mork, Dina Demner-Fushman
  
• COLE and LYS at BioASQ MESINESP Task: large scale multilabel text categorization with sparse and dense indices313-323
  Francisco J. Ribadas-Pena, Shuyuan Cao, Elmurod Kuriyozov
  
• LASIGE-BioTM at MESINESP2: entity linking with semantic similarity and extreme multi-label classification on Spanish biomedical documents324-334
  Pedro Ruas, Vitor D. T. Andrade, Francisco M. Couto
  
• NLM at BioASQ Synergy 2021: Deep Learning-based Methods for Biomedical Semantic Question Answering about COVID-19335-350
  Mourad Sarrouti, Deepak Gupta, Asma Ben Abacha, Dina Demner-Fushman
  
• KU-DMIS at BioASQ 9: Data-centric and model-centric approaches for biomedical question answering351-359
  Wonjin Yoon, Jaehyo Yoo, Sumin Seo, Mujeen Sung, Minbyul Jeong, Gangwoo Kim, Jaewoo Kang
  
• NCU-IISR/AS-GIS: Results of Various Pre-trained Biomedical Language Models and Linear Regression Model in BioASQ Task 9b Phase B360-368
  Yu Zhang, Jen-Chieh Han, Richard Tzong-Han Tsai
  

CheckThat!: Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

• Overview of the CLEF-2021 CheckThat! Lab Task 1 on Check-Worthiness Estimation in Tweets and Political Debates369-392
  Shaden Shaar, Maram Hasanain, Bayan Hamdan, Zien Sheikh Ali, Fatima Haouari, Alex Nikolov, Mucahid Kutlu, Yavuz Selim Kartal, Firoj Alam, Giovanni Da San Martino, Alberto Barrón-Cedeño, Ruben Miguez, Javier Beltrán, Tamer Elsayed, Preslav Nakov
  
• Overview of the CLEF-2021 CheckThat! Lab Task 2 on Detecting Previously Fact-Checked Claims in Tweets and Political Debates393-405
  Shaden Shaar, Fatima Haouari, Watheq Mansour, Maram Hasanain, Nikolay Babulkov, Firoj Alam, Giovanni Da San Martino, Tamer Elsayed, Preslav Nakov
  
• Overview of the CLEF-2021 CheckThat! Lab: Task 3 on Fake News Detection406-423
  Gautam Kishore Shahi, Julia Maria Struß, Thomas Mandl
  
• QMUL-SDS at CheckThat! 2021: Enriching Pre-Trained Language Models for the Estimation of Check-Worthiness of Arabic Tweets424-429
  Amani S. Abumansour, Arkaitz Zubiaga
  
• SCUoL at CheckThat! 2021: An AraBERT Model for Check-Worthiness of Arabic Tweets430-434
  Saud Althabiti, Mohammad Alsalka, Eric Atwell
  
• M82B at CheckThat! 2021: Multiclass Fake News Detection Using BiLSTM435-445
  Sohel Siddique Ashik, Abdur Rahman Apu, Nusrat Jahan Marjana, Md. Sanzidul Islam, Md. Arid Hassan
  
• CIC at CheckThat! 2021: Fake News detection Using Machine Learning And Data Augmentation446-454
  Noman Ashraf, Sabur Butt, Grigori Sidorov, Alexander Gelbukh
  
• MUCIC at CheckThat! 2021: FaDo-Fake News Detection and Domain Identification using Transformers Ensembling455-464
  Fazlourrahman Balouchzahi, Hosahalli Lakshmaiah Shashirekha, Grigori Sidorov
  
• UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims465-475
  Ipek Baris Schlicht, Angel Felipe Magnossão de Paula, Paolo Rosso
  
• SU-NLP at CheckThat! 2021: Check-Worthiness of Turkish Tweets476-483
  Buse Carik, Reyyan Yeniterzi
  
• Aschern at CLEF CheckThat! 2021: Lambda-Calculus of Fact-Checked Claims484-493
  Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
  
• UAICS at CheckThat! 2021: Fake news detection494-507
  Ciprian Gabriel Cusmuliuc, Matei Alexandru Amarandei, Ioana Pelin, Vlad-Iulian Cociorva, Adrian Iftene
  
• University of Regensburg at CheckThat! 2021:Exploring Text Summarization for Fake News Detection508-519
  Philipp Hartl, Udo Kruschwitz
  
• CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification520-530
  Álvaro Huertas-García, Javier Huertas-Tato, Alejandro Martín, David Camacho
  
• Classifier for fake news detection and Topical Domain of News Articles531-536
  William Kana Tsoplefack
  
• Nkovachevich at CheckThat! 2021: BERT fine-tuning approach to fake news detection537-544
  Ninko Kovachevich
  
• NLP&IR@UNED at CheckThat! 2021: Check-worthiness estimation and fake news detection using transformer models545-557
  Juan R. Martinez-Rico, Juan Martinez-Romo, Lourdes Araujo
  
• DIPS at CheckThat! 2021: Verified Claim Retrieval558-571
  Simona Mihaylova, Iva Borisova, Dzhovani Chemishanov, Preslav Hadzhitsanev, Momchil Hardalov, Preslav Nakov
  
• NLytics at CheckThat! 2021: Multi-class fake news detection of news articles and domain identification with RoBERTa - a baseline model572-581
  Albert Pritzkau
  
• NLytics at CheckThat! 2021: Detecting Previously Fact-Checked Claims by Measuring Semantic Similarity582-591
  Albert Pritzkau
  
• NLytics at CheckThat! 2021: Check-Worthiness Estimation as a Regression Problem on Transformers592-602
  Albert Pritzkau
  
• NITK_NLP at CheckThat! 2021: Ensemble Transformer Model for Fake News Classification603-611
  Hariharan RamakrishnaIyer LekshmiAmmal, Anand Kumar Madasamy
  
• Team Sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine Learning612-618
  Abdullah Al Mamun Sardar, Shahalu Akter Salma, Md. Sanzidul Islam, Md. Arid Hasan, Touhid Bhuiyan
  
• Qword at CheckThat! 2021: An Extreme Gradient Boosting Approach for Multiclass Fake News Detection619-627
  Rudra Sarker Utsha, Mumenunnessa Keya, Md. Arid Hasan, Md. Sanzidul Islam
  
• GPLSI team at CheckThat! 2021: Fine-tuning BETO and RoBERTa628-638
  Robiert Sepúlveda-Torres, Estela Saquete
  
• BeaSku at CheckThat! 2021: Fine–Tuning Sentence BERT with Triplet Loss and Limited Data639-647
  Beata Skuczyńska, Shaden Shaar, Jennifer Spenader, Preslav Nakov
  
• BlackOps at CheckThat! 2021: User Profiles Analyze of Intelligent Detection on Fake Tweets Notebook for PAN648-658
  S. M. Sohan, Sharun Akter Khusbu, Md. Sanzidul Islam, Md. Arid Hasan
  
• Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation659-669
  Evan Williams, Paul Rodrigues, Sieu Tran
  
• TOBB ETU at CheckThat! 2021: Data Engineering for Detecting Check-Worthy Claims670-680
  Muhammed Said Zengin, Yavuz Selim Kartal, Mucahid Kutlu
  
• Fight for 4230 at CheckThat! 2021: Domain-Specific Preprocessing and Pretrained Model for Ranking Claims by Check-Worthiness681-692
  Xinrui Zhou, Bohuai Wu, Pascale Fung
  

ChEMU: Cheminformatics Elsevier Melbourne University lab

• Extended Overview of ChEMU 2021: Reaction Reference Resolution and Anaphora Resolution in Chemical Patents693-709
  Yuan Li, Biaoyan Fang, Jiayuan He, Hiyori Yoshikawa, Saber A Akhondi, Christian Druckenbrodt, Camilo Thorne, Zubair Afzal, Zenan Zhai, Timothy Baldwin, Karin Verspoor
  
• A pipelined approach to Anaphora Resolution in Chemical Patents710-719
  Ritam Dutt, Sopan Khosla, Carolyn Rose
  
• HUKB at ChEMU 2021 Task 2: Anaphora Resolution720-731
  Kojiro Machi, Masaharu Yoshioka
  

eHealth: CLEFeHealth

• Overview of CLEF eHealth Task 1 - SpRadIE: A challenge on information extraction from Spanish Radiology Reports732-750
  Viviana Cotik, Laura Alonso Alemany, Darío Filippo, Franco Luque, Roland Roller, Jorge Vivaldi, Ammer Ayach, Fernando Carranza, Lucas Defrancesca, Antonella Dellanzo, Macarena Fernández Urquiza
  
• Consumer Health Search at CLEF eHealth 2021751-769
  Lorraine Goeuriot, Hanna Suominen, Gabriella Pasi, Elias Bassani, Nicola Brew-Sam, Gabriela Nicole González Sáez, Liadh Kelly, Philippe Mulhem, Sandaru Seneviratne, Rishabh Upadhyay, Marco Viviani, Chenchen Xu
  
• IMS-UNIPD @ CLEF eHealth Task 1: A Memory Based Reproducible Baseline770-774
  Giorgio Maria Di Nunzio
  
• IMS-UNIPD @ CLEF eHealth Task 2: Reciprocal Ranking Fusion in CHS775-779
  Giorgio Maria Di Nunzio, Federica Vezzani
  
• LSI_UNED at CLEF eHealth2021: Exploring the effects of transfer learning in negation detection and entity recognition in clinical texts780-793
  Hermenegildo Fabregat, Andres Duque, Lourdes Araujo, Juan Martinez-Romo
  
• Pre-trained language models to extract information from radiological reports794-803
  Pilar López-Úbeda, Manuel Carlos Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia
  
• Extracting information from radiology reports by Natural Language Processing and Deep Learning804-817
  Miguel Ángel Martín-Caro García-Largo, Isabel Segura-Bedmar
  
• Comparing Transformer-based NER approaches for analysing textual medical diagnoses818-833
  Marco Polignano, Marco de Gemmis, Giovanni Semeraro
  
• Information Extraction from Spanish Radiology Reports using multilingual BERT834-845
  Oswaldo Solarte-Pabón, Orlando Montenegro, Alberto Blazquez-Herranz, Hadi Saputro, Alejandro Rodriguez-González, Ernestina Menasalvas
  
• A multi-BERT hybrid system for Named Entity Recognition in Spanish radiology reports846-856
  Víctor Suárez-Paniagua, Hang Dong, Arlene Casey
  
• Learning to rank for Consumer Health Search857-863
  Hua Yang, Xiaoming Liu, Binbin Zheng, Guan Yang
  

eRisk: Early Risk Prediction on the Internet

• Overview of eRisk at CLEF 2021: Early Risk Prediction on the Internet (Extended Overview)864-887
  Javier Parapar, Patricia Martín-Rodilla, David E. Losada, Fabio Crestani
  
• Predicting Sign of Depression via Using Frozen Pre-trained Models and Random Forest Classifier888-896
  Hassan Alhuzali, Tianlin Zhang, Sophia Ananiadou
  
• VADER meets BERT: sentiment analysis for early detection of signs of self-harm through social mining897-907
  Lucas Barros, Alina Trifan, José Luis Oliveira
  
• UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet908-927
  Angelo Basile, Mara Chinea-Rios, Ana-Sabina Uban, Thomas Müller, Luise Rössler, Seren Yenikent, Berta Chulví, Paolo Rosso, Marc Franco-Salvador
  
• Exploring the Performance of Baseline Text Mining Frameworks for Early Prediction of Self Harm Over Social Media928-937
  Tanmay Basu, Georgios V Gkoutos
  
• Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT938-949
  Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu
  
• NLP-UNED at eRisk 2021: self-harm early risk detection with TF-IDF and linguistic features950-965
  Elena Campillo-Ageitos, Hermenegildo Fabregat, Lourdes Araujo, Juan Martinez-Romo
  
• uOttawa at eRisk 2021: Automatic Filling of the Beck's Depression Inventory Questionnaire using Deep Learning966-980
  Diana Inkpen, Ruba Skaik, Prasadith Buddhitha, Dimo Angelov, Maxwell Thomas Fredenburgh
  
• CeDRI at eRisk 2021: A Naive Approach to Early Detection of Psychological Disorders in Social Media981-991
  Rui Pedro Lopes
  
• UNSL at eRisk 2021: A Comparison of Three Early Alert Policies for Early Risk Detection992-1021
  Juan Martín Loyola, Sergio Burdisso, Horacio Thompson, Leticia Cagnina, Marcelo Errecalde
  
• UniOR NLP at eRisk 2021: Assessing the Severity of Depression with Part of Speech and Syntactic Features1022-1030
  Raffaele Manna, Johanna Monti
  
• Early Detection of Signs of Pathological Gambling, Self-Harm and Depression through Topic Extraction and Neural Networks1031-1045
  Diego Maupomé, Maxime D. Armstrong, Fanny Rancourt, Thomas Soulas, Marie-Jean Meurs
  
• Transfer Learning for Automated Responses to the BDI Questionnaire1046-1058
  Christoforos Spartalis, George Drosatos, Avi Arampatzis
  
• Towards transfer learning using BERT for early detection of self-harm of social media users 1059-1070
  Qamar Un Nisa, Rafi Muhammad
  
• A RoBERTa-based model on measuring the severity of the signs of depression1071-1080
  Shih-Hung Wu, Zhao-Jun Qiu
  

ImageCLEF: Multimedia Retrieval Challenge in CLEF

• Overview of the VQA-Med Task at ImageCLEF 2021: Visual Question Answering and Generation in the Medical Domain1081-1088
  Asma Ben Abacha, Mourad Sarrouti, Dina Demner-Fushman, Sadid A. Hasan, Henning Müller
  
• Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type Classification1089-1100
  Serge Kozlovski, Vitali Liauchuk, Yashin Dicente Cid, Vassili Kovalev, Henning Müller
  
• Overview of the ImageCLEFmed 2021 Concept & Caption Prediction Task1101-1112
  Obioma Pelka, Asma Ben Abacha, Alba Garcia Seco de Herrera, Janadhip Jacutprakart, Christoph M. Friedrich, Henning Müller
  
• Overview of ImageCLEFcoral 2021: Coral Reef Image Annotation of a 3D Environment1113-1120
  Jon Chamberlain, Alba García Seco De Herrera, Antonio Campello, Adrian Clark, Thomas Oliver, Hassan Moustahfid
  
• Overview of the 2021 ImageCLEFdrawnUI Task: Detection and Recognition of Hand Drawn and Digital Website UIs1121-1132
  Raul Berari, Andrei Tăuteanu, Dimitri Fichou, Paul Brie, Mihai Dogariu, Liviu-Daniel Ștefan, Mihai Gabriel Constantin, Bogdan Ionescu
  
• Classification of Tuberculosis Type on CT Scans of Lungs using a fusion of 2D and 3D Deep Convolutional Neural Networks1133-1144
  Emad Aghajanzadeh, Behzad Shomali, Diba Aminshahidi, Navid Ghassemi
  
• Simple Neural Network based TB Classification1145-1150
  Anirudh Anand, Karthik Raja Anandan, Bhuvana Jayaraman, Mirnalinee Thanga Nadar Thanga Thai
  
• ImageCLEF 2021: Deep categorizing tuberculosis cases using normalization and pseudo-color CT image1151-1159
  Tetsuya Asakawa, Riku Tsuneda, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono
  
• Attention-based CNN-GRU Model For Automatic Medical Images Captioning: ImageCLEF 20211160-1173
  Djamila-Romaissa Beddiar, Mourad Oussalah, Tapio Seppänen
  
• PUC Chile team at Caption Prediction: ResNet visual encoding and caption classification with Parametric ReLU1174-1183
  Vicente Castro, Pablo Pino, Denis Parra, Hans Lobel
  
• AUEB NLP Group at ImageCLEFmed Caption Tasks 20211184-1200
  Foivos Charalampakos, Vasilis Karatzas, Vasiliki Kougia, John Pavlopoulos, Ion Androutsopoulos
  
• Chabbiimen at VQA-Med 2021: Visual Generation of Relevant Natural Language Questions from Radiology Images for Anomaly Detection1201-1210
  Imen Chebbi
  
• TeamS at VQA-Med 2021: BBN-Orchestra for Long-tailed Medical Visual Question Answering1211-1217
  Sedigheh Eslami, Gerard de Melo, Christoph Meinel
  
• SYSU-HCP at VQA-Med 2021: A Data-centric Model with Efficient Training Methodology for Medical Visual Question Answering1218-1228
  Haifan Gong, Ricong Huang, Guanqi Chen, Guanbin Li
  
• A Convolutional Neural Networks based Coral Reef Annotation and Localization1229-1238
  Rohit Gunti, Abebe Rorissa
  
• UI element detection from wireframe drawings of websites1239-1252
  Prasang Gupta, Vishakha Bansal
  
• UAIC2021: Lung Analysis for Tuberculosis Classification1253-1263
  Alexandra Hanganu, Cristian Simionescu, Lucia-Georgiana Coca, Adrian Iftene
  
• NLIP-Essex-ITESM at ImageCLEFcaption 2021 task : Deep Learning-based Information Retrieval and Multi-label Classification towards improving Medical Image Understanding1264-1274
  Janadhip Jacutprakart, Francisco Parrilla Andrade, Rodolfo Cuan, Arely Aceves Compean, Giorgos Papanastasiou, Alba Garcia Seco de Herrera
  
• Lijie at ImageCLEFmed VQA-Med 2021: Attention Model-based Efficient Interaction between Multimodality1275-1284
  Jie Li, Shengyan Liu
  
• Lijie at ImageCLEFmed Tuberculosis 2021: EfficientNet Simplified Tuberculosis Case Classification1285-1294
  Jie Li, Li Yang, Yang Bai
  
• TAM at VQA-Med 2021: A Hybrid Model with Feature Extraction and Fusion for Medical Visual Question Answering1295-1304
  Yong Li, Zhenguo Yang, Tianyong Hao
  
• Multi-Classification Study of the Tuberculosis with 3D CBAM-ResNet and EfficientNet1305-1309
  Xing Lu, Eric Y Chang, Chun-Nan Hsu, Jiang Du, Amilcare Gentili
  
• Identifying tuberculosis type in CTs1310-1316
  Cosmin Moisii, Radu Miron, Mihaela Breaban
  
• AEHRC CSIRO at ImageCLEFmed Caption 20211317-1328
  Aaron Nicolson, Jason Dowling, Bevan Koopman
  
• SSN MLRG at VQA-MED 2021: An Approach for VQA to Solve Abnormality Related Queries using Improved Datasets1329-1335
  Noor Mohamed Sheerin Sitara, Srinivasan Kavitha
  
• PUC Chile team at TBT Task: Diagnosis of Tuberculosis Type using segmented CT scans1336-1345
  José Miguel Quintana, Daniel Florea, Ria Deane, Denis Parra, Pablo Pino, Pablo Messina, Hans Löbel
  
• PUC Chile team at VQA-Med 2021: approaching VQA as a classification task via fine-tuning a pretrained CNN1346-1351
  Ricardo Schilling, Pablo Messina, Denis Parra, Hans Löbel
  
• PUC Chile team at Concept Detection: K Nearest Neighbors with Perceptual Similarity1352-1358
  Gregory Schuit, Vicente Castro, Pablo Pino, Denis Parra, Hans Lobel
  
• Automatic Coral Reef Annotation, Localization and Pixel-wise Parsing Using Mask R-CNN1359-1364
  Lukáš Soukup
  
• Kdelab at ImageCLEF 2021: Medical Caption Prediction with Effective Data Pre-processing and Deep Learning1365-1374
  Riku Tsuneda, Tetsuya Asakawa, Masaki Aono
  
• Improving web user interface element detection using Faster R-CNN1375-1386
  Jiří Vyskočil, Lukáš Picek
  
• ImageSem Group at ImageCLEFmed Caption 2021 Task: Exploring the Clinical Significance of the Textual Descriptions Derived from Medical Images 1387-1393
  Xuwen Wang, Zhen Guo, Chunyuan Xu, Lianglong Sun, Jiao Li
  
• Pixelwise annotation of coral reef substrates1394-1404
  Jessica Wright, Ioana-Lia Palosanu, Louis Clift, Alba García Seco de Herrera, Jon Chamberlain
  
• Yunnan University at VQA-Med 2021: Pretrained BioBERT for Medical Domain Visual Question Answering1405-1411
  Qian Xiao, Xiaobing Zhou, Ya Xiao, Kun Zhao
  
• ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scans1412-1421
  Hasib Zunair, Aimon Rahman, Nabeel Mohammed
  

LifeCLEF: Multimedia Life Species Identification

• Overview of PlantCLEF 2021: cross-domain plant identification1422-1436
  Hervé Goëau, Pierre Bonnet, Alexis Joly
  
• Overview of BirdCLEF 2021: Bird call identification in soundscape recordings1437-1450
  Stefan Kahl, Tom Denton, Holger Klinck, Hervé Glotin, Hervé Goëau, Willem-Pier Vellinga, Robert Planqué, Alexis Joly
  
• Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images1451-1462
  Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, Alexis Joly
  
• Overview of SnakeCLEF 2021: Automatic Snake Species Identification with Country-Level Focus1463-1476
  Lukáš Picek, Andrew Durso, Isabelle Bolon, Rafael Ruiz de Castaneda
  
• EfficientNets and Vision Transformers for Snake Species Identification Using Image and Location Information1477-1498
  Louise Bloch, Christoph M. Friedrich
  
• Incorporation of object detection models and location data into snake species classification1499-1511
  Regő Borsodi, Dávid Papp
  
• A Deep Learning Method for Visual Recognition of Snake Species1512-1525
  Rail Chamidullin, Milan Šulc, Jiří Matas, Lukáš Picek
  
• Improved Herbarium-Field Triplet Network for Cross-Domain Plant Identification: NEUON Submission to LifeCLEF 2021 Plant1526-1539
  Sophia Chulif, Yang Loong Chang
  
• UAIC-AI at SnakeCLEF 2021: Impact of convolutions in snake species recognition1540-1546
  Lucia Georgiana Coca, Alexia Theodora Popa, Razvan Contantin Croitoru, Luciana Paraschiva Bejan, Adrian Iftene
  
• Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution1547-1558
  Marcos V. Conde, Kumar Shubham, Prateek Agnihotri, Nitin D. Movva, Szilard Bessenyei
  
• Bird-Species Audio Identification, Ensembling 1D + 2D Signals1559-1570
  Gyanendra Das, Saksham Aggarwal
  
• Snake Species Classification using Transfer Learning Technique1571-1578
  Karthik Desingu, Mirunalini Palaniappan, Jitesh Kumar
  
• Recognizing bird species in diverse soundscapes under weak supervision1579-1586
  Christof Henkel, Pascal Pfeiffer, Philipp Singer
  
• Automatic Snake Classification using Deep Learning Algorithm 1587-1596
  Lekshmi Kalinathan, Prabavathy Balasundaram, Pradeep Ganesh, Sandeep Sekhar Bathala, Rahul Kumar Mukesh
  
• Birdcall Identification Using CNN and Gradient Boosting Decision Trees with Weak and Noisy Supervision1597-1608
  Naoki Murakami, Hajime Tanaka, Masataka Nishimori
  
• STFT Transformers for Bird Song Recognition1609-1616
  Jean-Francois Puget
  
• TUC Media Computing at BIRDCLEF 2021: Noise augmentation strategies in bird sound classification in combination with DenseNets and ResNets1617-1626
  Arunodhayan Sampathkumar, Danny Kowerko
  
• Learning to Monitor Birdcalls From Weakly-Labeled Focused Recordings1627-1638
  Jan Schlüter
  
• Contrastive Representation Learning for Natural World Imagery: Habitat prediction for 30,000 species1639-1648
  Sachith Seneviratne
  
• BirdCLEF 2021: building a birdcall segmentation model based on weak labels1649-1658
  Maxim Shugaev, Naoya Tanahashi, Philip Dhingra, Urvish Patel
  
• Weighted Pseudo Labeling Refinement for Plant Identification1659-1667
  Youshan Zhang, Brian Davison
  

LiLAS: Living Labs for Academic Search

• Overview of LiLAS 2021 – Living Labs for Academic Search (Extended Overview)1668-1699
  Philipp Schaer, Timo Breuer, Leyla Jael Castro, Benjamin Wolff, Johann Schaible, Narges Tavakolpoursaleh
  
• TEKMA at CLEF-2021: BM-25 based rankings for scientific publication retrieval and data set recommendation1700-1711
  Jüri Keller, Leon Paul Mondrian Munz
  
• PyTerrier-based Research Data Recommendations for Scientific Articles in the Social Sciences1712-1722
  Narges Tavakolpoursaleh, Johann Schaible
  
• Ad-hoc Retrieval of scientific Documents on the LIVIVO Search Portal1723-1742
  Anh Huy Matthias Tran, Andreas Kruff, Joshua Thos, Constantin Krah, Michelle Reiners, Fabian Ax, Saskia Brech, Sascha Gharib, Verena Pawlas
  

PAN: Lab on Digital Text Forensics and Stylometry

• Overview of the Cross-Domain Authorship Verification Task at PAN 20211743-1759
  Mike Kestemont, Enrique Manjavacas, Ilia Markov, Janek Bevendorff, Matti Wiegmann, Efstathios Stamatatos, Benno Stein, Martin Potthast
  
• Overview of the Style Change Detection Task at PAN 20211760-1771
  Eva Zangerle, Maximilian Mayerl, Martin Potthast, Benno Stein
  
• Profiling Hate Speech Spreaders on Twitter Task at PAN 20211772-1789
  Francisco Rangel, Gretel Liz De La Peña Sarracén, Berta Chulvi, Elisabetta Fersini, Paolo Rosso
  
• Profiling Hate Spreaders using word N-grams1790-1795
  Jorge Alcañiz, José Andrés
  
• Profiling Hate Speech Spreaders by Classifying Micro Texts Using BERT Model1796-1800
  Esam Alzahrani, Leon Jololian
  
• Profiling Hate Speech Spreaders on Twitter1801-1807
  Àngel Andújar Carracedo, Raquel Jiménez Mondéjar
  
• Identify Hate Speech Spreaders on Twitter using Transformer Embeddings Features and AutoML Classifiers1808-1812
  Talha Anwar
  
• Profiling Haters on Twitter using Statistical and Contextualized Embeddings1813-1821
  Hamed Babaei Giglou, Taher Rahgooy, Jafar Razmara, Mostafa Rahgouy, Zahra Rahgooy
  
• Profiling Spreaders of Hate Speech with N-grams and RoBERTa1822-1828
  Christopher Bagdon
  
• HSSD: Hate Speech Spreader Detection using N-grams and Voting Classifier1829-1836
  Fazlourrahman Balouchzahi, Shashirekha Hosahalli Lakshmaiah, Grigori Sidorov
  
• Unified and Multilingual Author Profiling for Detecting Haters1837-1845
  Ipek Baris Schlicht, Angel Felipe Magnossão de Paula
  
• O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification1846-1857
  Benedikt Bönninghoff, Robert Nickel, Dorothea Kolossa
  
• INFOTEC-LaBD at PAN@CLEF21: Profiling Hate Speech Spreaders on Twitter through Emotion-based Representations1858-1870
  Hiram Cabrera, Sabino Miranda, Eric Tellez
  
• Exploiting Contextualized Word Representations to Profile Haters on Twitter1871-1882
  Tanise Ceron, Camilla Casula
  
• Use of Lexical and Psycho-Emotional Information to Detect Hate Speech Spreaders on Twitter - Notebook for PAN at CLEF 20211883-1891
  Riccardo Cervero
  
• Profiling Hate Speech Spreaders on Twitter1892-1898
  Kumar Gourav Das, Buddhadeb Garai, Srijan Das, Braja Gopal Patra
  
• Style Change Detection on Real-World Data using an LSTM-powered Attribution Algorithm1899-1909
  Robert Deibel, Denise Loefflad
  
• Detection of Hate Speech Spreaders with BERT1910-1919
  David Dukić, Ana Sović Kržić
  
• Graph-based Siamese Network for Authorship Verification1920-1930
  Daniel Embarcadero-Ruiz, Helena Gómez-Adorno, Ivan Reyes-Hernández, Alexis García, Alberto Embarcadero-Ruiz
  
• Profiling Hate Speech Spreaders using Characters andWords N-grams1931-1936
  Daniel Espinosa, Grigori Sidorov
  
• Hate speech spreader detection using contextualized word embeddings1937-1944
  Evgeny Finogeev, Mariam Kaprielova, Artem Chashchin, Kirill Grashchenkov, George Gorbachev, Oleg Bakhteev
  
• Author classification as pre-training for pairwise authorship verification1945-1952
  Romain Futrzynski
  
• Profiling Hate Speech Spreaders on Twitter using stylistic features and word embeddings1953-1962
  Lucía Gómez Zaragozá, Sara Hinojosa Pinto
  
• Profiling Hate Speech Spreaders on Twitter: Transformers and mixed pooling1963-1975
  Álvaro Huertas-García, Javier Huertas-Tato, Alejandro Martín, David Camacho
  
• Effective Detection of Hate Speech Spreaders on Twitter1976-1986
  Julian Höllig, Yeong Su Lee, Nina Seemann, Michaela Geierhos
  
• UniNE at PAN-CLEF 20211987-1994
  Catherine Ikae
  
• UniNE at PAN-CLEF 2021: Authorship Verification1995-2003
  Catherine Ikae
  
• Early Detection of Online Hate Speech Spreaders with Learned User Representations2004-2010
  Darius Irani, Avyakta Wrat, Silvio Amir
  
• Profiling Hate Speech Spreaders on Twitter2011-2024
  Rakshita Jain, Devanshi Goel, Prashant Sahu, Abhinav Kumar, Jyoti Prakash Singh
  
• Using N-grams and Statistical Features to Identify Hate Speech Spreaders on Twitter2025-2034
  Eszter Katona, Jakab Buda, Flora Bolonyai
  
• Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification. Notebook for PAN at CLEF 20212035-2046
  Roberto Labadie, Daniel Castro-Castro, Reynier Ortega Bueno
  
• HaMor at the Profiling Hate Speech Spreaders on Twitter2047-2055
  Mirko Lai, Marco Antonio Stranisci, Cristina Bosco, Rossana Damiano, Viviana Patti
  
• Authorship Verification based on Lucene architecture2056-2059
  Zhihao Liao, Yong Han, Leilei Kong, Zhuopeng Hong, Zijian Li, Guiyuan Liang, Zhenwei Mo, Zhixian Li, Zhongyuan Han
  
• Hate Speech Detection on Twitter2060-2063
  Carolina Martin del Campo Rodriguez, Grigori Sidorov, Ildar Batyrshin
  
• Authorship Verification with neural networks via stylometric feature concatenation2064-2068
  Antonio Menta, Ana Garcia-Serrano
  
• Dual Neural Network Classification Based on BERT Feature Extraction for Authorship Verification2069-2072
  Xiaogang Miao, Haoliang Qi, Zhijie Zhang, Guiyuan Cao, Ruilan Lin, Wenbin Lin
  
• Style change detection using Siamese neural networks2073-2082
  Sukanya Nath
  
• University of Regensburg @ PAN: Profiling Hate Speech Spreaders on Twitter2083-2089
  Kwabena Odame Akomeah, Udo Kruschwitz, Bernd Ludwig
  
• Local Classification with Recurrent Neural Network for Profiling Hate Speech Spreaders on Twitter. Notebook for PAN at CLEF 20212090-2102
  Pablo Pallarés, Carlos Herrero
  
• Encoding Text Information By Pre-trained Model For Authorship Verification2103-2107
  Zeyang Peng, Leilei Kong, Zhijie Zhang, Zhongyuan Han, Xu Sun
  
• Feature Similarity-based Regression Models for Authorship Verification2108-2117
  Marina Pinzhakova, Tom Yagel, Jakov Rabinovits
  
• Phonetic Detection for Hate Speech Spreaders on Twitter2118-2125
  Edwin Puertas, Juan Carlos Martinez-Santos
  
• Detection of hate speech spreaders using convolutional neural networks2126-2136
  Marco Siino, Elisa Di Nuovo, Ilenia Tinnirello, Marco La Cascia
  
• Writing Style Change Detection on Multi-Author Documents2137-2145
  Rhia Singh, Janith Weerasinghe, Rachel Greenstadt
  
• Multi-label Style Change Detection by Solving a Binary Classification Problem2146-2157
  Eivind Strøm
  
• Multi-level stacked ensemble with sparse and dense features for hate speech detection on Twitter2158-2168
  Darko Tosev, Sonja Gievska
  
• Siamese Bert for Authorship Verification2169-2177
  Jacob Tyo, Bhuwan Dhingra, Zachary Lipton
  
• Detecting Hate Speech Spreaders on Twitter using LSTM and BERT in English and Spanish2178-2185
  Moshe Uzan, Yaakov Hacohen-Kerner
  
• Profiling Hate Speech Spreaders on Twitter: Exploiting Textual Analysis of Tweets and Combination of Textual Representations2186-2192
  Claudio Moisés Valiense de Andrade, Marcos André Gonçalves
  
• Profiling Hate Speech Spreaders on Twitter: SVM vs. Bi-LSTM2193-2200
  Inna Vogel, Meghana Meghana
  
• Feature Vector Difference based Authorship Verification for Open-World Settings2201-2207
  Janith Weerasinghe, Rhia Singh, Rachel Greenstadt
  
• Style Change Detection Based On Writing Style Similarity2208-2211
  Zhijie Zhang, Zhongyuan Han, Leilei Kong, Xiaogang Miao, Zeyang Peng, Jieming Zeng, Haojie Cao, Jinxi Zhang, Ziwei Xiao, Xuemei Peng
  

SimpleText: (Re)Telling right scientific stories to non-specialists via text simplification

• Overview of SimpleText CLEF 2021 Workshop and Pilot Tasks2212-2227
  Liana Ermakova, Patrice Bellot, Pavel Braslavski, Jaap Kamps, Josiane Mothe, Diana Nurbakova, Irina Ovchinnikova, Eric Sanjuan
  
• Overview of the SimpleText Workshop at INFORSID-2021: Scientific Text Simplification and Popularization2228-2232
  Liana Ermakova, Josiane Mothe, Eric Sanjuan
  
• Multimodal science communication: from documentary research to infographic via mind mapping2233-2236
  Sílvia Araújo, Radia Hannachi
  
• LIA@SimpleText2021: automatic query extraction from press outlets for mining related readable passages in scientific literature2237-2241
  Malek Hajjem, Eric Sanjuan
  
• What Science-Related Topics Need to Be Popularized? A Comparative Study2242-2255
  Irina Ovchinnikova, Diana Nurbakova, Liana Ermakova
  
• Importance of Data and Controllability in Neural Text Simplification2256-2257
  Wei Xu
  

Touché: Argument Retrieval

• Overview of Touché 2021: Argument Retrieval2258-2284
  Alexander Bondarenko, Lukas Gienapp, Maik Fröbe, Meriem Beloucif, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, Matthias Hagen
  
• Exploring Argument Retrieval for Controversial Questions Using Retrieve and Re-rank Pipelines2285-2291
  Raunak Agarwal, Andrei Koniaev, Robin Schaefer
  
• Learning to Rank Arguments with Feature Selection2292-2301
  Christopher Akiki, Maik Fröbe, Matthias Hagen, Martin Potthast
  
• Development of an IR System for Argument Search2302-2318
  Marco Alecci, Tommaso Baldo, Luca Martinelli, Elia Ziroldo
  
• DistilBERT-based Argumentation Retrieval for Answering Comparative Questions2319-2330
  Alaa Alhamzeh, Mohamed Bouhaouel, Elöd Egyed-Zsigmond, Jelena Mitrović
  
• Document retrieval task on controversial topic with Re-Ranking approach2331-2353
  Andrea Cassetta, Alberto Piva, Enrico Vicentini
  
• Retrieving Comparative Arguments using Ensemble Methods and BERT2354-2365
  Viktoriia Chekalina, Alexander Panchenko
  
• Quality-aware Argument Retrieval with Topical Clustering2366-2373
  Lukas Gienapp
  
• Exploring BERT Synonyms and Quality Prediction for Argument Retrieval2374-2388
  Tommaso Green, Luca Moroldo, Alberto Valente
  
• Touché Task 2: Comparative Argument Retrieval A document-based search engine for answering comparative questions2389-2402
  Daniel Helmrich, Denis Streitmatter, Fionn Fuchs, Maximilian Heykeroth
  
• Argument Retrieval for Comparative Questions based on independent features2403-2416
  Thi Kim Hanh Luu, Jan-Niklas Weder
  
• Exploring Document Expansion for Argument Retrieval2417-2422
  Alina Mailach, Denise Arnold, Stefan Eysoldt, Simon Kleine
  
• A Search Engine System for Touché Argument Retrieval task to answer Controversial Questions2423-2440
  Edoardo Raimondi, Marco Alessio, Nicola Levorato
  
• Team Skeletor at Touché 2021: Argument Retrieval and Visualization for Controversial Questions2441-2454
  Kevin Ros, Carl Edwards, Heng Ji, Chengxiang Zhai
  
• Thor at Touché 2021: Argument Retrieval for Comparative Questions2455-2462
  Ekaterina Shirshakova, Ahmad Wattar