CLEF 2022
CLEF 2022 Working Notes

Table of Contents

• Preface
  Guglielmo Faggioli, Nicola Ferro, Allan Hanbury, Martin Potthast
  

ARQMath: Answer Retrieval for Questions on Math

• Overview of ARQMath-3 (2022): Third CLEF Lab on Answer Retrieval for Questions on Math (Working Notes Version)1-27
  Behrooz Mansouri, Vít Novotný, Anurag Agarwal, Douglas W. Oard, Richard Zanibbi
  
• Diverse Semantics Representation is King28-39
  Martin Geletka, Vojtěch Kalivoda, Michal Štefánik, Marek Toma, Petr Sojka
  
• Dowsing for Answers to Math Questions: Doing Better with Less40-62
  Andrew Kane, Yin Ki Ng, Frank Wm. Tompa
  
• Expanding Spatial Regions and Incorporating IDF for PHOC-Based Math Formula Retrieval at ARQMath-363-82
  Matt Langsenkamp, Behrooz Mansouri, Richard Zanibbi
  
• DPRL Systems in the CLEF 2022 ARQMath Lab: Introducing MathAMR for Math-Aware Search83-103
  Behrooz Mansouri, Douglas W. Oard, Richard Zanibbi
  
• Combining Sparse and Dense Information Retrieval104-118
  Vít Novotný, Michal Štefánik
  
• Transformer-Encoder and Decoder Models for Questions on Math119-137
  Anja Reusch, Maik Thiele, Wolfgang Lehner
  
• Formula Retrieval Using Structural Similarity138-146
  Sandip Sarkar, Dipankar Das, Partha Pakray, David Pinto
  
• Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, CLEF147-170
  Wei Zhong, Yuqing Xie, Jimmy Lin
  

BioASQ: Large-scale biomedical semantic indexing and question answering

• Overview of BioASQ Tasks 10a, 10b and Synergy10 in CLEF2022171-178
  Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Georgios Paliouras
  
• Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources179-203
  Antonio Miranda-Escalada, Luis Gascó, Salvador Lima-López, Eulàlia Farré-Maduell, Darryl Estrada, Anastasios Nentidis, Anastasia Krithara, Georgios Katsimpras, Georgios Paliouras, Martin Krallinger
  
• Deep Learning solutions based on fixed contextualized embeddings from PubMedBERT on BioASQ 10b and traditional IR in Synergy204-221
  Tiago Almeida, André Pinho, Rodrigo Pereira, Sérgio Matos
  
• Exploring Biomedical Question Answering with BioM-Transformers At BioASQ10B challenge: Findings and Techniques222-234
  Sultan Alrowili, K. Vijay-Shanker
  
• DIAGÑOZA: a Natural Language Processing Tool for Automatic Annotation of Clinical Free Text with SNOMED-CT235-243
  Matic Bernik, Robert Tovornik, Borut Fabjan, Luis Marco-Ruiz
  
• HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate Retrieval244-258
  Florian Borchert, Matthieu-P. Schapranow
  
• A Simple Terminology-Based Approach to Clinical Entity Recognition259-264
  José Castaño, Laura Gambarte, Carlos Otero, Daniel Luna
  
• SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentions265-273
  Mariia Chizhikova, Jaime Collado-Montañez, Pilar López-Úbeda, Manuel C. Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia
  
• LaRSA at BioASQ 10b: classical and novel approaches for biomedical document retrieval and question answering274-280
  Zakaria Kaddari, Toumi Bouchentouf
  
• Zero-shot Hybrid Retrieval and Reranking Models for Biomedical Literature281-290
  Jing Lu, Ji Ma, Keith Hall
  
• BioTABQA: Instruction Learning for Biomedical Table Question Answering291-304
  Man Luo, Sharad Saxena, Swaroop Mishra, Mihir Parmar, Chitta Baral
  
• Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering305-314
  Diego Mollá
  
• Biomedical Spanish Language Models for entity recognition and linking at BioASQ DisTEMIST315-324
  Vincenzo Moscato, Marco Postiglione, Giancarlo Sperlì
  
• Unicage at DISTEMIST - Named Entity Recognition system using only Bash and Unicage tools325-334
  André Neves
  
• ELECTROLBERT: Combining Replaced Token Detection and Sentence Order Prediction335-340
  Martin Reczko
  
• Clinical Named Entity Recognition and Linking using BERT in Combination with Spanish Medical Embeddings341-349
  Javier Reyes-Aguillón, Rodrigo del Moral, Orlando Ramos-Flores, Helena Gómez-Adorno, Gemma Bel-Enguix
  
• mBERT and Simple Post-Processing: A Baseline for Disease Mention Detection in Spanish350-356
  Antonio Tamayo, Diego A. Burgos, Alexander Gelbukh
  
• NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improve the Performance of Pretrained Language Model in BioASQ 10b Phase B357-367
  Hao-Hsuan Ting, Yu Zhang, Jen-Chieh Han, Richard Tzong-Han Tsai
  

CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection

• Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in Tweets368-392
  Preslav Nakov, Alberto Barrón-Cedeño, Giovanni Da San Martino, Firoj Alam, Rubén Míguez, Tommaso Caselli, Mucahid Kutlu, Wajdi Zaghouani, Chengkai Li, Shaden Shaar, Hamdy Mubarak, Alex Nikolov, Yavuz Selim Kartal
  
• Overview of the CLEF-2022 CheckThat! Lab Task 2 on Detecting Previously Fact-Checked Claims393-403
  Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Shaden Shaar, Hamdy Mubarak, Nikolay Babulkov
  
• Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News Detection404-421
  Juliane Köhler, Gautam Kishore Shahi, Julia Maria Struß, Michael Wiegand, Melanie Siegel, Thomas Mandl, Mina Schütz
  
• PoliMi-FlatEarthers at CheckThat! 2022: GPT-3 applied to claim detection422-427
  Stefano Agresti, S. Amin Hashemian, Mark J. Carman
  
• SCUoL at CheckThat! 2022: Fake News Detection Using Transformer-Based Models428-433
  Saud Althabiti, Mohammad Ammar Alsalka, Eric Atwell
  
• CIC at CheckThat! 2022: Multi-class and Cross-lingual Fake News Detection434-443
  Muhammad Arif, Atnafu Lambebo Tonja, Iqra Ameer, Olga Kolesnikova, Alexander Gelbukh, Grigori Sidorov, Abdul Gafar Manuel Meque
  
• CODE at CheckThat! 2022: Multi-class fake news detection of news articles with BERT444-455
  Olivier Blanc, Albert Pritzkau, Ulrich Schade, Michaela Geierhos
  
• Zorros at CheckThat! 2022: Ensemble Model for Identifying Relevant Claims in Tweets456-467
  Nicu Buliga, Madalina Raschip
  
• NUS-IDS at CheckThat!2022: Identifying Check-worthiness of Tweets using CheckthaT5468-477
  Mingzhe Du, Sujatha Das Gollapalli, See-Kiong Ng
  
• TOBB ETU at CheckThat! 2022: Detecting Attention-Worthy and Harmful Tweets and Check-Worthy Claims478-491
  Ahmet Bahadir Eyuboglu, Mustafa Bora Arslan, Ekrem Sonmezer, Mucahid Kutlu
  
• Fraunhofer SIT at CheckThat! 2022: Ensemble Similarity Estimation for Finding Previously Fact-Checked Claims492-499
  Raphael Antonius Frick, Inna Vogel
  
• Fraunhofer SIT at CheckThat! 2022: Semi-Supervised Ensemble Classification for Detecting Check-Worthy Tweets500-510
  Raphael Antonius Frick, Inna Vogel, Isabella Nunes Grieser
  
• SimBa at CheckThat! 2022: Lexical and Semantic Similarity Based Detection of Verified Claims in an Unsupervised and Supervised Way511-531
  Alica Hövelmeyer, Katarina Boland, Stefan Dietze
  
• RUB-DFL at CheckThat! 2022: Transformer Models and Linguistic Features for Identifying Relevant Claims532-545
  Zehra Melce Hüsünbeyi, Oliver Deck, Tatjana Scheffler
  
• VTU_BGM at Check That! 2022: An Autoregressive Encoding Model for Detecting Check-worthy Claims546-553
  Sanjana Kavatagi, Rashmi Rachh, Madhura Mulimani
  
• Text_Minor at CheckThat! 2022: Fake News Article Detection Using RoBERT554-563
  Sujit Kumar, Gaurav Kumar, Sanasam Ranbir Singh
  
• BUM at CheckThat! 2022: A Composite Deep Learning Approach to Fake News Detection using Evidence Retrieval564-572
  David La Barbera, Kevin Roitero, Joel Mackenzie, Damiano Spina, Gianluca Demartini, Stefano Mizzaro
  
• COURAGE at CheckThat! 2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRA573-583
  Francesco Lomonaco, Gregor Donabauer, Marco Siino
  
• FoSIL at CheckThat! 2022: Using Human Behaviour-Based Optimization for Text Classification584-594
  Andy Ludwig, Jenny Felser, Jian Xi, Dirk Labudde, Michael Spranger
  
• NLPIR-UNED at CheckThat! 2022: Ensemble of Classifiers for Fake News Detection595-605
  Juan R. Martinez-Rico, Juan Martinez-Romo, Lourdes Araujo
  
• CoulterOzler at CheckThat! 2022: Detecting fake news with transformers606-615
  Kadir Bulut Ozler, Riah Coulter
  
• HBDCI at CheckThat! 2022: Fake News Detection Using a Combination of stylometric Features and Deep Learning616-628
  Claudia Porto-Capetillo, Diego Lecuona-Gómez, Helena Gómez-Adorno, Ignacio Arroyo-Fernández, Jair Neri-Chávez
  
• NLytics at CheckThat! 2022: Hierarchical multi-class fake news detection of news articles exploiting the topic structure629-648
  Albert Pritzkau, Olivier Blanc, Michaela Geierhos, Ulrich Schade
  
• NITK-IT_NLP at CheckThat! 2022: Window based approach for Fake News Detection using transformers649-655
  Hariharan Ramakrishnaiyer Lekshmiammal, Anand Kumar Madasamy
  
• AI Rational at CheckThat! 2022: Using transformer models for tweet classification656-659
  Aleksandar Savchev
  
• AIT_FHSTP at CheckThat! 2022: Cross-Lingual Fake News Detection with a Large Pre-Trained Transformer660-670
  Mina Schütz, Jaqueline Böck, Medina Andresel, Armin Kirchknopf, Daria Liakhovets, Djordje Slijepčević, Alexander Schindler
  
• RIET Lab at CheckThat! 2022: Improving Decoder based Re-ranking for Claim Matching671-678
  Michael Shliselberg, Shiri Dori-Hacohen
  
• Asatya at CheckThat! 2022: Multimodal BERT for Identifying Claims in Tweets679-693
  Manan Suri, Prajeet Katari, Saumay Dudeja
  
• iCompass at CLEF2022 CheckThat! Lab: Combining Deep Language Models for Fake News Detection694-701
  Bilel Taboubi, Mohamed Aziz Ben Nessir, Hatem Haddad
  
• iCompass at CheckThat! 2022: ARBERT and AraBERT for Arabic Checkworthy Tweet Identification702-709
  Bilel Taboubi, Mohamed Aziz Ben Nessir, Hatem Haddad
  
• Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text710-721
  Prerona Tarannum, Md. Arid Hasan, Firoj Alam, Sheak Rashed Haider Noori
  
• ARC-NLP at CheckThat! 2022: Contradiction for Harmful Tweet Detection722-739
  Cagri Toraman, Oguzhan Ozcelik, Furkan Şahinuç, Umitcan Sahin
  
• ur-iw-hnt at CheckThat! 2022: Cross-lingual Text Summarization for Fake News Detection740-748
  Hoai Nam Tran, Udo Kruschwitz
  
• Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer749-757
  Ciprian-Octavian Truică, Elena-Simona Apostol, Adrian Paschke
  

ChEMU: Cheminformatics Elsevier Melbourne University lab

• Extended Overview of ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents758-781
  Yuan Li, Biaoyan Fang, Jiayuan He, Hiyori Yoshikawa, Saber A. Akhondi, Christian Druckenbrodt, Camilo Thorne, Zubair Afzal, Zenan Zhai, Kojiro Machi, Masaharu Yoshioka, Youngrok Jang, Hosung Song, Junho Lee, Gyeonghun Kim, Yireun Kim, Stanley Jungkyu Choi, Honglak Lee, Kyunghoon Bae, Darshini Mahendran, Christina Tang, Bridget McInnes, Timothy Baldwin, Karin Verspoor
  
• Context aware Named Entity Recognition and Relation Extraction with Domain-specific language model782-796
  Youngrok Jang, Hosung Song, Junho Lee, Gyeonghun Kim, Yireun Kim, Stanley Jungkyu Choi, Honglak Lee, Kyunghoon Bae
  
• HUKB at ChEMU 2022 Task 1: Expression-Level Information Extraction797-807
  Kojiro Machi, Masaharu Yoshioka
  
• NLPatVCU: CLEF 2022 ChEMU Shared Task System Description808-820
  Darshini Mahendran, Christina Tang, Bridget T. McInnes
  

eRisk: Early risk prediction on the Internet

• Overview of eRisk 2022: Early Risk Prediction on the Internet (Extended Overview)821-850
  Javier Parapar, Patricia Martin-Rodilla, David E. Losada, Fabio Crestani
  
• An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder851-863
  Ana-Maria Bucur, Adrian Cosma, Liviu P. Dinu, Paolo Rosso
  
• UNED-MED at eRisk 2022: depression detection with TF-IDF, linguistic features and Embeddings864-874
  Elena Campillo-Ageitos, Juan Martinez-Romo, Lourdes Araujo
  
• Early detection of depression using BERT and DeBERTa875-882
  Sreegeethi Devaguptam, Thanmai Kogatam, Nishka Kotian, Anand Kumar M
  
• CLEF eRisk 2022: Detecting Early Signs of Pathological Gambling using ML and DL models with dataset chunking883-893
  Tudor-Andrei Dumitrașcu
  
• UNED-NLP at eRisk 2022: Analyzing gambling disorders in Social Media using Approximate Nearest Neighbors894-904
  Hermenegildo Fabregat, Andres Duque, Lourdes Araujo, Juan Martinez-Romo
  
• Early risk detection of mental illnesses using various types of textual features905-920
  Rodrigo Ferreira, Alina Trifan, José Luís Oliveira
  
• Sunday Rockers at eRisk 2022: Early Detection of Depression921-935
  Raluca-Andreea Gînga, Andrei-Alexandru Manea, Bogdan-Mihai Dobre
  
• Measuring the Severity of the Signs of Eating Disorders Using Similarity-Based Models936-946
  Seyed Habib Hosseini Saravani, Lancelot Normand, Diego Maupomé, Fanny Rancourt, Thomas Soulas, Sara Besharati, Anaelle Normand, Sébastien Mosser, Marie-Jean Meurs
  
• UNSL at eRisk 2022: Decision policies with history for early classification947-960
  Juan Martín Loyola, Horacio Thompson, Sergio Burdisso, Marcelo Errecalde
  
• SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing961-971
  Alba María Mármol-Romero, Salud María Jiménez-Zafra, Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, María-Teresa Martín-Valdivia, Arturo Montejo-Ráez
  
• NLP-IISERB@eRisk2022: Exploring the Potential of Bag of Words, Document Embeddings and Transformer Based Framework for Early Prediction of Eating Disorder, Depression and Pathological Gambling Over Social Media972-986
  Harshvardhan Srivastava, Lijin N S, Sruthi S, Tanmay Basu
  
• ZHAW at eRisk 2022: Predicting Signs of Pathological Gambling - GloVe for Snowy Days987-994
  Samuel Stalder, Erman Zankov
  
• LauSAn at eRisk 2022: Simply and Effectively Optimizing Text Classification for Early Detection995-1004
  Andreas Säuberli, Sooyeon Cho, Laura Stahlhut
  
• Early detection of depression with linear models using hand-crafted and contextual features1005-1013
  Ilija Tavchioski, Blaž Škrlj, Senja Pollak, Boshko Koloski
  
• CYUT at eRisk 2022: Early Detection of Depression Based-on Concatenating Representation of Multiple Hidden Layers of RoBERTa Model1014-1025
  Shih-Hung Wu, Zhao-Jun Qiu
  
• TUA1 at eRisk 2022: Exploring Affective Memories for Early Detection of Depression1026-1037
  Kang Xin, Dou Rongyu, Yu Haitao
  

HIPE: Named Entity Recognition and Linking in Multilingual Historical Documents

• Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents1038-1063
  Maud Ehrmann, Matteo Romanello, Sven Najem-Meyer, Antoine Doucet, Simon Clematide
  
• Knowledge-based Contexts for Historical Named Entity Recognition & Linking1064-1078
  Emanuela Boros, Carlos-Emiliano González-Gallardo, Edward Giamphy, Ahmed Hamdi, José G. Moreno, Antoine Doucet
  
• Entity Linking in Multilingual Newspapers and Classical Commentaries with BERT1079-1089
  Kai Labusch, Clemens Neudecker
  
• Exploring Transformers for Multilingual Historical Named Entity Recognition1090-1108
  Anja Ryser, Quynh Anh Nguyen, Niclas Bodenmann, Shih-Yun Chen
  
• hmBERT: Historical Multilingual Language Models for Named Entity Recognition1109-1129
  Stefan Schweter, Luisa März, Katharina Schmid, Erion Çano
  

iDPP: Intelligent Disease Progression Prediction

• Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction Challenge1130-1210
  Alessandro Guazzo, Isotta Trescato, Enrico Longato, Enidia Hazizaj, Dennis Dosso, Guglielmo Faggioli, Giorgio Maria Di Nunzio, Gianmaria Silvello, Martina Vettoretti, Erica Tavazzi, Chiara Roversi, Pietro Fariselli, Sara C. Madeira, Mamede de Carvalho, Marta Gromicho, Adriano Chiò, Umberto Manera, Arianna Dagliati, Giovanni Birolo, Helena Aidos, Barbara Di Camillo, Nicola Ferro
  
• Hierarchical Modelling for ALS Prognosis: Predicting the Progression Towards Critical Events1211-1227
  Ruben Branco, Diogo F. Soares, Andreia S. Martins, Eleonora Auletta, Eduardo N. Castanho, Susana Nunes, Filipa Serrano, Rita T. Sousa, Catia Pesquita, Sara C. Madeira, Helena Aidos
  
• Evaluation of XAI on ALS 6-months mortality prediction1228-1235
  Tommaso Mario Buonocore, Giovanna Nicora, Arianna Dagliati, Enea Parimbelli
  
• Predicting the Risk of & Time to Impairment for ALS patients1236-1255
  Aidan Mannion, Thierry Chevalier, Didier Schwab, Lorraine Goeuriot
  
• Explaining Artificial Intelligence Predictions of Disease Progression with Semantic Similarity1256-1268
  Susana Nunes, Rita T. Sousa, Filipa Serrano, Ruben Branco, Diogo F. Soares, Andreia S. Martins, Eleonora Auletta, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos, Catia Pesquita
  
• Multi-Event Survival Prediction for Amyotrophic Lateral Sclerosis1269-1276
  Corrado Pancotti, Giovanni Birolo, Tiziana Sanavia, Cesare Rollo, Piero Fariselli
  
• Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression1277-1293
  Isotta Trescato, Alessandro Guazzo, Enrico Longato, Enidia Hazizaj, Chiara Roversi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo
  

ImageCLEF: Multimedia Retrieval Challenge

• Overview of ImageCLEFmedical 2022 – Caption Prediction and Concept Detection1294-1307
  Johannes Rückert, Asma Ben Abacha, Alba G. Seco de Herrera, Louise Bloch, Raphael Brüngel, Ahmad Idrissi-Yaghir, Henning Schäfer, Henning Müller, Christoph M. Friedrich
  
• Overview of ImageCLEFtuberculosis 2022 - CT-based Cavern Detection and Report1308-1317
  Serge Kozlovski, Yashin Dicente Cid, Vassili Kovalev, Henning Müller
  
• ImageCLEFcoral task: Coral reef image annotation and localisation1318-1328
  Jon Chamberlain, Alba García Seco De Herrera, Antonio Campello, Adrian Clark
  
• Overview of the ImageCLEF 2022 Aware Task1329-1338
  Adrian Popescu, Jérôme Deshayes-Chossart, Hugo Schindler, Bogdan Ionescu
  
• Overview of ImageCLEFfusion 2022 Task - Ensembling Methods for Media Interestingness Prediction and Result Diversification1339-1346
  Liviu-Daniel Ștefan, Mihai Gabriel Constantin, Mihai Dogariu, Bogdan Ionescu
  
• Caverns Detection and Caverns Report in Tuberculosis: lesion detection based on image using YOLO-V3 and median based multi-label multi-class classification using SRGAN1347-1354
  Tetsuya Asakawa, Riku Tsuneda, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono
  
• AUEB NLP Group at ImageCLEFmed Caption 20221355-1373
  Foivos Charalampakos, Giorgos Zachariadis, John Pavlopoulos, Vasilis Karatzas, Christoforos Trakas, Ion Androutsopoulos
  
• AIMultimediaLab at ImageCLEFfusion 2022: DeepFusion Methods for Ensembling in Diverse Scenarios1374-1380
  Mihai Gabriel Constantin, Liviu-Daniel Ștefan, Mihai Dogariu, Bogdan Ionescu
  
• CMRE-UoG team at ImageCLEFmedical Caption 2022: Concept Detection and Image Captioning1381-1390
  Francesco Dalla Serra, Fani Deligianni, Jeffrey Dalton, Alison Q O'Neil
  
• ImageCLEFmedical Caption Task, Concept Detection, Finding Duplicates, SDVA-UCSD Approach1391-1395
  Amilcare Gentili
  
• Dual Convolutional Neural Networks and Regression model based Coral Reef Annotation and Localization1396-1412
  Rohit Raj Gunti, Abebe Rorissa
  
• IUST_NLPLAB at ImageCLEFmedical Caption Tasks 20221413-1434
  Malihe Hajihosseini, Yasaman Lotfollahi, Melika Nobakhtian, Mohammad Mahdi Javid, Fateme Omidi, Sauleh Eetemadi
  
• A Fusion Approach for Web Search Result Diversification using Machine Learning Algorithms1435-1442
  Lekshmi Kalinathan, Prabavathy Balsundaram, Yogesh Munees E, Siddharth S, Shrijith Mr, Shrikeshavinee Ramachandran, Shruti Sriram, Ramdhanush Venkatakrishnan
  
• Monitoring Coral Reefs Using Faster R-CNN1443-1454
  Felix Kerlin, Kirill Bogomasov, Stefan Conrad
  
• CSIRO at ImageCLEFmedical Caption 20221455-1473
  Leo Lebrat, Aaron Nicolson, Rodrigo Santa Cruz, Gregg Belous, Bevan Koopman, Jason Dowling
  
• Semi-supervised Multi-Label Classification with 3D CBAM Resnet for Tuberculosis Cavern Report1474-1479
  Xing Lu, An Yan, Eric Y Chang, Chun-Nan Hsu, Julian McAuley, Jiang Du, Amilcare Gentili
  
• Polimi-ImageClef Group at ImageCLEFmedical Caption task 20221480-1486
  Seyyed Ali Mir Ghayyomnia, Kai de Gast, Mark J. Carman
  
• NeuralDynamicsLab at ImageCLEFmedical 20221487-1504
  Georgios Moschovis, Erik Fransén
  
• SSN MLRG at ImageCLEFmedical Caption 2022: Medical Concept Detection and Caption Prediction using Transfer Learning and Transformer based Learning Approaches1505-1515
  Sheerin Sitara Noor Mohamed, Kavitha Srinivasan
  
• SSN CSE at ImageCLEFaware 2022: Contextual Job Search Feedback Score based on Photographic Profile using a Random Forest Regression Technique1516-1524
  Aarthi Nunna, Aravind Kannan Rathinasapabathi, Chirag Bheemaiah P K, Kavitha Srinivasan
  
• CS_Morgan at ImageCLEFmedical 2022 Caption Task: Deep Learning Based Multi-Label Classification and Transformers for Concept Detection & Caption Prediction1525-1534
  Md Mahmudur Rahman, Oyebisi Layode
  
• Detecting Concepts and Generating Captions from Medical Images: Contributions of the VCMI Team to ImageCLEFmedical 2022 Caption1535-1553
  Isabel Rio-Torto, Cristiano Patrício, Helena Montenegro, Tiago Gonçalves
  
• SSN MLRG at ImageCLEF 2022 Tuberculosis: Caverns Report using 3D CNN and Uniformizing Techniques1554-1561
  Dheepak S, Kavitha Srinivasan, Raghuraman G
  
• A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness1562-1571
  Maria Shoukat, Khubaib Ahmad, Naina Said, Nasir Ahmad, Mohammed Hasanuzzaman, Kashif Ahmad
  
• Automated Classification of Lung Tuberculosis Using 3D Deep Convolutional Neural Networks1572-1579
  Sushaanth Srinivasan, Sharvesh Shankar, Nitheesh Kumar N, Sabarivasan Velayutham, Thejas N, Vikash Anand N, Lekshmi Kalinathan, Prabavathy Balasundaram
  
• Ensembled Approach for Web Search Result Diversification Using Neural Networks1580-1589
  Shreya Sriram, Madhuri Mahalingam, Sarah Aymen Naseer, Shajith Hameed, Rahul Rajagopalan, Sai Shashaank R, Lekshmi Kalinathan, Prabavathy Balasundaram
  
• Multi Regressor Based User Rating Predictor for ImageCLEF Aware 20221590-1595
  Aarthi Suresh Kumar, Anirudh A, Jeet Golecha M, Karthik Raja A, Bhuvana Jayaraman, Mirnalinee T.T.
  
• Kdelab at ImageCLEFmedical 2022 Caption Prediction Task1596-1607
  Riku Tsuneda, Tetsuya Asakawa, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono
  
• Kdelab at ImageCLEFmedical 2022: Medical Concept Detection with Image Retrieval and Code Ensemble1608-1618
  Riku Tsuneda, Tetsuya Asakawa, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono
  
• ImageSem Group at ImageCLEFmedical Caption 2022 task: Generating Medical Image Descriptions based on Vision-Language Pre-training1619-1625
  Xuwen Wang, Jiao Li
  
• CSIRO at the ImageCLEFmedical 2022 Tuberculosis Caverns Detection Challenge: A 2D and 3D Deep Learning Detection Network Approach1626-1640
  Bowen Xin, Hang Min, Ashley G Gillman, Bevan Koopman, Jason Dowling, Aaron Nicolson
  

JokeR: Automatic Wordplay and Humour Translation

• Overview of the CLEF 2022 JOKER Task 1: Classify and Explain Instances of Wordplay1641-1665
  Liana Ermakova, Fabio Regattin, Tristan Miller, Anne-Gwenn Bosser, Silvia Araújo, Claudine Borg, Gaëlle Le Corre, Julien Boccou, Albin Digue, Aurianne Damoy, Paul Campen, Orlane Puchalski
  
• Overview of the CLEF 2022 JOKER Task 2: Translate Wordplay in Named Entities1666-1680
  Liana Ermakova, Tristan Miller, Julien Boccou, Albin Digue, Aurianne Damoy, Paul Campen
  
• Overview of the CLEF 2022 JOKER Task 3: Pun Translation from English into French1681-1700
  Liana Ermakova, Fabio Regattin, Tristan Miller, Anne-Gwenn Bosser, Claudine Borg, Benoît Jeanjean, Élise Mathurin, Gaëlle Le Corre, Radia Hannachi, Silvia Araújo, Julien Boccou, Albin Digue, Aurianne Damoy
• Wordplay location and interpretation with deep learning methods1701-1705
  Hakima Arroubat
  
• Using ‘punning schemes’ as a template for translating wordplay1706-1718
  Julien Boccou
  
• Poetic or Humorous Text Generation: Jam event at PFIA20221719-1726
  Anne-Gwenn Bosser, Liana Ermakova, Florence Dupin de Saint-Cyr, Pierre De Loor, Victor Charpennay, Nicolas Pépin-Hermann, Benoit Alcaraz, Jean-Victor Autran, Alexandre Devillers, Juliette Grosset, Aymeric Hénard, Florian Marchal-Bornert
  
• A history of classification and JokeR's reach1727-1734
  Paul Campen, Albin Digue
  
• Translation strategies: adaptation and equivalence — Joker contest1735-1740
  Aurianne Damoy
  
• Translating, transcribing, transmitting and transcending a pun: why playing with words is far from being punless/pointless.1741-1749
  Charlotte Daniel, Noémie Vandenborre
  
• A translation-oriented categorisation of wordplays1750-1755
  Michel Delarche
  
• FAST-MT Participation for the JOKER CLEF-2022 Automatic Pun and Humour Translation Tasks1756-1773
  Farhan Dhanani, Muhammad Rafi, Muhammad Atif Tahir
  
• Automatic translation of wordplay1774-1786
  Albin Digue, Paul Campen
  
• Using machine learning to classify and interpret wordplay1787-1792
  Aygyul Epimakhova
  
• Humorous Wordplay Generation in French1793-1806
  Loic Glemarec, Anne-Gwenn Bosser, Julien Boccou, Liana Ermakova
  
• Use of SimpleT5 for the CLEF workshop JokeR: Automatic Pun and Humor Translation1807-1817
  Loïc Glemarec
  
• LJGG @ CLEF JOKER Task 3: An improved solution joining with dataset from task 11818-1827
  Leopoldo Jesús Gutiérrez Galeano
  
• Assessing Wordplay-Pun classification from JOKER dataset with pretrained BERT humorous models1828-1833
  Victor Manuel Palma Preciado, Grigori Sidorov, Carolina Palma Preciado
  
• Automatic Translation and Wordplay: An Amateur’s (Playful) Thoughts1834-1841
  Fabio Regattin
  
• Greenpeace Memes for Communicating Climate Change1842-1845
  Yuliya Samofalova, Andrea Catellani, Louise-Amélie Cougnon
  
• How good can an automatic translation of Pokémon names be?1846-1848
  Léa Talec-Bernard
  

LeQua: Learning to Quantify

• A Detailed Overview of LeQua@CLEF 2022: Learning to Quantify1849-1868
  Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani, Gianluca Sperduti
  
• UniOviedo(Team2) at LeQua 2022: Comparison of traditional quantifiers and a new method based on Energy Distance1869-1874
  Juan José del Coz
  
• UniPadova @ LeQua 2022: A Preliminary Study of a BM25 Approach to Quantification1875-1880
  Giorgio Maria Di Nunzio
  
• UniOviedo(Team1) at LeQua 2022: Sample-based quantification using deep learning1881-1888
  Pablo González
  
• UniLeiden at LeQua 2022: The first step in understanding the behaviour of the median sweep quantifier using continuous sweep1889-1904
  Kevin Kloos, Quinten A Meertens, Julian D Karch
  
• KULeuven at LeQua 2022: Model Calibration in Quantification Learning1905-1910
  Teodora Popordanoska, Matthew B. Blaschko
  
• DortmundAI at LeQua 2022: Regularized SLD1911-1915
  Martin Senz, Mirko Bunse
  

LifeCLEF: Biodiversity identification and prediction Challenges

• Overview of PlantCLEF 2022: Image-based plant identification at global scale1916-1928
  Hervé Goëau, Pierre Bonnet, Alexis Joly
  
• Overview of BirdCLEF 2022: Endangered bird species recognition in soundscape recordings1929-1939
  Stefan Kahl, Amanda Navine, Tom Denton, Holger Klinck, Patrick Hart, Hervé Glotin, Hervé Goëau, Willem-Pier Vellinga, Robert Planqué, Alexis Joly
  
• Overview of GeoLifeCLEF 2022: Predicting Species Presence from Multi-modal Remote Sensing, Bioclimatic and Pedologic Data1940-1956
  Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, Alexis Joly
  
• Overview of SnakeCLEF 2022: Automated Snake Species Identification on a Global Scale1957-1969
  Lukáš Picek, Marek Hruz, Andrew M. Durso, Isabelle Bolon
  
• Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem1970-1981
  Lukáš Picek, Milan Šulc, Jiří Matas, Jacob Heilmann-Clausen
  
• Combination of Object Detection, Geospatial Data, and Feature Concatenation for Snake Species Identification1982-2013
  Louise Bloch, Jan-Frederick Böckmann, Benjamin Bracke, Christoph M. Friedrich
  
• Extreme Automatic Plant Identification Under Constrained Resources2014-2024
  Jose Carranza-Rojas, Ruben Gonzalez-Villanueva, Kelvin Jimenez-Morales, Kevin Quesada-Montero, Esteban A. Esquivel-Barboza, Nicole Carvajal-Barboza
  
• A Global-Scale Plant Identification using Deep Learning: NEUON Submission to PlantCLEF 20222025-2035
  Sophia Chulif, Sue Han Lee, Yang Loong Chang
  
• Few-shot Long-Tailed Bird Audio Recognition2036-2046
  Marcos V. Conde, Ui-Jin Choi
  
• Classification of Fungi Species: A Deep Learning Based Image Feature Extraction and Gradient Boosting Ensemble Approach2047-2062
  Karthik Desingu, Anirudh Bhaskar, Mirunalini Palaniappan, Eeswara Anvesh Chodisetty, Haricharan Bharathi
  
• Does Closed-Set Training Generalize to Open-Set Recognition?2063-2077
  Fan Gao, Zining Chen, Weiqiu Wang, Yinan Song, Fei Su, Zhicheng Zhao, Hong Chen
  
• Plant and Animal Species Prediction at Certain Locations with Convolutional Neural Networks2078-2086
  Juntao Jiang
  
• Classification of Plant Species Using AlexNet Architecture2087-2093
  Pravinkrishnan K, Naren Sivakumar, Ainsely Jebaraj, C Padma Pooja, Supraja Sridhar, Prabavathy Balasundaram, Lekshmi Kalinathan
  
• Plant Species Identification using Probability Tree Approach of Deep Learning Models2094-2102
  Anantharaman Karun, Krishnakumar Divyasri, Sella Veluswami Jansi Rani
  
• Block Label Swap for Species Distribution Modelling2103-2114
  Benjamin Kellenberger, Tuia Devis
  
• Bird Species Classification: One Step at a Time2115-2122
  Sidhart Krishnan, Priya Khandelwal, Rhythm Garg
  
• Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural Networks2123-2150
  César Leblanc, Alexis Joly, Titouan Lorieul, Maximilien Servajean, Pierre Bonnet
  
• Dealing with Class Imbalance in Bird Sound Classification2151-2158
  Eduard Martynov, Yuuichiroh Uematsu
  
• Motif Mining and Unsupervised Representation Learning for BirdCLEF 20222159-2167
  Anthony Miyaguchi, Jiangyue Yu, Bryan Cheungvivatpant, Dakota Dudley, Aniketh Swain
  
• Image-based plant identification with taxonomy aware architecture2168-2174
  Jack Min Ong, Sze Jue Yang, Kam Woh Ng, Chee Seng Chan
  
• Deep Learning and Gradient Boosting Ensembles for Classification of Snake Species2175-2188
  Mirunalini Palaniappan, Karthik Desingu, Haricharan Bharathi, Eeswara Anvesh Chodisetty, Anirudh Bhaskar
  
• TUC Media Computing at BirdCLEF 2022: Strategies in identifying bird sounds in a complex acoustic environments2189-2198
  Arunodhayan Sampathkumar, Danny Kowerko
  
• When Large Kernel Meets Vision Transformer: A Solution for SnakeCLEF & FungiCLEF2199-2211
  Yang Shen, Xuhao Sun, Zijian Zhu
  
• Convolution Neural Network Fine-tuning for Plant and Animal Species Distribution Modelling2212-2218
  Mélisande Teng, Sal Elkafrawy
  
• Transformer-based Fine-Grained Fungi Classification in an Open-Set Scenario2219-2226
  Stefan Wolf, Jürgen Beyerer
  
• 1st Place Solution for FungiCLEF 2022 Competition: Fine-grained Open-set Fungi Recognition2227-2237
  Zihua Xiong, Yumeng Ruan, Yifei Hu, Yue Zhang, Yuke Zhu, Sheng Guo, Bing Han
  
• Transfer Learning with Self-Supervised Vision Transformer for Large-Scale Plant Identification2238-2252
  Mingle Xu, Sook Yoon, Yongchae Jeong, Jaesu Lee, Dong Sun Park
  
• Solution for SnakeCLEF 2022 by Tackling Long-tailed Categorization2253-2261
  Lingfeng Yang, Xiang Li, Renjie Song, Kexin Zhu, Gang Li
  
• Efficient Model Integration for Snake Classification2262-2274
  Jun Yu, Hao Chang, Zhongpeng Cai, Guochen Xie, Liwen Zhang, Keda Lu, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang
  
• Bag of Tricks and a Strong Baseline for FGVC2275-2290
  Jun Yu, Hao Chang, Keda Lu, Guochen Xie, Liwen Zhang, Zhongpeng Cai, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang
  
• Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 20222291-2300
  Cheng Zou, Furong Xu, Meng Wang, Wen Li, Yuan Cheng
  

PAN Lab on Digital Text Forensics and Stylometry

• Overview of the Authorship Verification Task at PAN 20222301-2313
  Efstathios Stamatatos, Mike Kestemont, Krzysztof Kredens, Piotr Pezik, Annina Heini, Janek Bevendorff, Benno Stein, Martin Potthast
  
• Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO). Overview for PAN at CLEF 20222314-2343
  Reynier Ortega Bueno, Berta Chulvi, Francisco Rangel, Paolo Rosso, Elisabetta Fersini
  
• Overview of the Style Change Detection Task at PAN 20222344-2356
  Eva Zangerle, Maximilian Mayerl, Martin Potthast, Benno Stein
  
• Ensemble-Based Clustering for Writing Style Change Detection in Multi-Authored Textual Documents2357-2374
  Shams Alshamasi, Mohamed Menai
  
• Style Change Detection using Discourse Markers2375-2380
  Faisal Alvi, Hasan Algafri, Naif Alqahtani
  
• User profiling: voting scheme2381-2390
  Maria Fernanda Artigas Herold, Daniel Castro Castro
  
• Profiling Irony and Stereotype Spreaders with Encoding Dependency Information using Graph Convolutional Network2391-2401
  Hamed Babaei Giglou, Mostafa Rahgouy, Ali Rahmati, Taher Rahgooy, Cheryl D Seals
  
• CIC@PAN: Simplifying Irony Profiling using Twitter Data2402-2410
  Sabur Butt, Fazlourrahman Balouchzahi, Grigori Sidorov, Alexander Gelbukh
  
• A Multi-Model Voting Ensemble Classifier based on BERT for Profiling Irony and Stereotype Spreaders on Twitter2411-2415
  Haojie Cao, Zhongyuan Han, Zhenwei Mo, Zengyao Li, Ziwei Xiao, Zijian Li, Leilei Kong
  
• A content spectral-based analysis for authorship verification2416-2425
  Melesio Crespo-Sanchez, Helena Gómez-Adorno, Ivan Lopez-Arevalo, Edwin Aldana-Bobadilla, Karla Salas-Jimenez, Jorge Cortes-Lopez
  
• An SVM Ensemble Approach to Detect Irony and Stereotype Spreaders on Twitter2426-2432
  Daniele Croce, Domenico Garlisi, Marco Siino
  
• Irony and Stereotype Spreading Author Profiling on Twitter using Machine Learning: A BERT-TFIDF based Approach2433-2444
  Amit Das, Nilanjana Raychawdhary, Gerry Dozier, Cheryl D. Seals
  
• UMUTeam at IROSTEREO: Profiling Irony and Stereotype spreaders on Twitter combining Linguistic Features with Transformers2445-2454
  José Antonio García Díaz, Miguel Ángel Rodríguez-García, Francisco García-Sánchez, Rafael Valencia-Garcia
  
• An Unorthodox Approach for Style Change Detection2455-2466
  Lukas Graner, Paul Ranly
  
• BERT Sentence Embeddings in different Machine Learning and Deep Learning Models for Author Profiling applied to Irony and Stereotype Spreaders on Twitter2467-2474
  Claudia Gómez, Daniel Parres
  
• Profiling Irony Speech Spreaders on Social Networks Using Deep Cleaning and BERT2475-2481
  Leila Hazrati, Alireza Sokhandan, Leili Farzinvash
  
• Exploiting Affective-based Information for Profiling Ironic Users on Twitter2482-2490
  Delia Irazu Hernandez Farias, Manuel Montes-Y-Gómez
  
• Authorship verification Based On Fully Interacted Text Segments2491-2495
  Mingjie Huang, Leilei Kong, Zeyang Peng, Yihui Ye, Zengyao Li, Xinyin Jiang, Zhongyuan Han
  
• Profiling Irony and Stereotype Spreaders with Language Models and Bayes' Theorem2496-2505
  Xinting Huang
  
• UniNE at PAN-CLEF 2022: Profiling Irony and Stereotype Spreaders on Twitter2506-2514
  Catherine Ikae
  
• Lexicon-Based Profiling of Irony and Stereotype Spreaders2515-2525
  Hyewon Jang
  
• Style Change Detection: Method Based On Pre-trained Model And Similarity Recognition2526-2531
  Xinyin Jiang, Haoliang Qi, Zhijie Zhang, Mingjie Huang
  
• Different Encoding Approaches for Authorship Verification 2532-2540
  Stefanos Konstantinou, Jinqiao Li, Angelos Zinonos
  
• Graph-Based Profile Condensation for Users Profiling2541-2553
  Roberto Labadie-Tamayo, Daniel Castro-Castro
  
• Style Change Detection Based On Bert And Conv1d2554-2559
  Qidi Lao, Li Ma, Wenyin Yang, Zexian Yang, Dong Yuan, Zhenlin Tan, Langzhang Liang
  
• Application of BERT in author verification task2560-2564
  Ziwang Lei, Haoliang Qi, Han Y, Zeyang Peng, Mingjie Huang
  
• Ensemble Pre-trained Transformer Models for Writing Style Change Detection2565-2573
  Tzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng, Lung-Hao Lee
  
• A BERT-based model for Profiling Irony and Stereotype Spreaders on Twitter2574-2577
  Wenbin Lin, Zhongyuan Han, Jinxi Zhang, Zengyao Li, Guiyuan Cao, Jianhong Yu, Leilei Kong
  
• Profiling Irony and Stereotype Spreaders on Twitter Using TF-IDF and Neural Network 2578-2584
  Haolong Ma, Dingjia Li, Yutong Sun
  
• Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural Network2585-2593
  Stefano Mangione, Marco Siino, Giovanni Garbo
  
• Graph-Based Siamese Network for Authorship Verification2594-2606
  Jorge Alfonso Martinez Galicia, Daniel Embarcadero Ruiz, Alejandro Ríos Orduña, Helena Gómez Adorno
  
• Text-to-Text Transformer in Authorship Verification Via Stylistic and Semantical Analysis2607-2616
  Maryam Najafi, Ehsan Tavan
  
• Use pre-trained models and multi-classifier voting methods to identify the ironic authors on Twitter2617-2622
  Jian Qin, Leilei Kong, Zhaoqian Huang, Jialin Huang, Yansheng Guo, Mingjie Huang, Zeyang Peng
  
• Irony &Stereotype Spreader Detection using Random Forests2623-2641
  Tiago Filipe Nunes Ribeiro, Yana Nikolaeva Nikolova, Kaja Seraphina Elisa Hano
  
• Three Style Similarity: sentence-embedding, auxiliary words, punctuation2642-2652
  Carlos A. Rodríguez-Losada, Daniel Castro-Castro
  
• Isolated Profile Style Representation2653-2660
  Carlos A. Rodríguez-Losada, Daniel Castro-Castro
  
• Profiling Irony and Stereotype Spreaders on Twitter: PAN Shared Task (IROSTEREO) 20222661-2665
  Alvaro Rodríguez Sánchez, Martin Barroso Ordóñez
  
• T100: A modern classic ensemble to profile irony and stereotype spreaders2666-2674
  Marco Siino, Ilenia Tinnirello, Marco La Cascia
  
• Identifying Author Profiles Containing Irony or Spreading Stereotypes with SBERT and Emojis2675-2681
  Narjes Tahaei, Harsh Verma, Parsa Bagherzadeh, Farhood Farahnak, Nadia Sheikh, Sabine Bergler
  
• Profiling irony and stereotype spreaders on Twitter based on term frequency in tweets2682-2686
  Dhaval Taunk, Sagar Joshi, Vasudeva Varma
  
• Identifying Ironic Content Spreaders on Twitter using Psychometrics, Contextual and Ironic Features with Gradient Boosting Classifier2687-2697
  Ehsan Tavan, Maryam Najafi, Reza Moradi
  
• Notebook for PAN at CLEF 2022:Profiling Irony and Stereotype Spreaders on Twitter2698-2702
  Bin Wang, Hui Ning
  
• Profiling Irony and Stereotype Spreaders on Twitter with BERT2703-2707
  Yifan Xu, Hui Ning
  
• A Intelligent Detection Method for Irony and Stereotype Based on Hybird Neural Networks2708-2713
  Zexian Yang, Li Ma, Wenyin Yang, Qidi Lao, Zhenlin Tan
  
• Authorship Verification Using Convolutional Neural Network2714-2719
  Yihui Ye, Han Y, Zeyang Peng, Mingjie Huang, Leilei Kong, Zhongyuan Han
  
• BERT-based ironic authors profiling2720-2733
  Wentao Yu, Benedikt Boenninghoff, Dorothea Kolossa
  
• Analysis of Irony and Stereotype Spreaders Based On Convolutional Neural Networks2734-2738
  Dong Yuan, Wenyin Yang, Li Ma, Zhenlin Tan, Qidi Lao
  
• An Ensemble Machine Learning Classifier for Profiling Irony and Stereotype Spreaders on Twitter2739-2745
  Li Zengyao, Han Zhongyuan, Mingjie Huang, Leilei Kong
  
• Irony and Stereotype Spreaders Detection using BERT-large and AutoGulon2746-2752
  Yuning Zhang, Hui Ning
  
• Style Change Detection based on Prompt2753-2756
  Zhijie Zhang, Zhongyuan Han, Leilei Kong
  
• Style Change Detection Based On Bi-LSTM And Bert2757-2761
  Jiayang Zi, Ling Zhou, Zhengyao Liu
  

SimpleText: Automatic Simplification of Scientific Texts

• Overview of the CLEF 2022 SimpleText Task 1: Passage Selection for a Simplified Summary2762-2772
  Eric Sanjuan, Stéphane Huet, Jaap Kamps, Liana Ermakova
  
• Overview of the CLEF 2022 SimpleText Task 2: Complexity Spotting in Scientific Abstracts2773-2791
  Liana Ermakova, Irina Ovchinnikov, Jaap Kamps, Diana Nurbakova, Silvia Araújo, Radia Hannachi
  
• Overview of the CLEF 2022 SimpleText Task 3: Query Biased Simplification of Scientific Texts2792-2804
  Liana Ermakova, Irina Ovchinnikov, Jaap Kamps, Diana Nurbakova, Silvia Araújo, Radia Hannachi
  
• Searching for explanation of difficult scientific terms2805-2809
  Majda Ennaciri
  
• Assembly Models for SimpleText Task 2: Results from Wuhan University Research Group2810-2817
  Huang Jianfei, Mao Jin
  
• Controllable Sentence Simplification Using Transfer Learning2818-2825
  Antonio Menta, Ana Garcia-Serrano
  
• Using a Pre-trained SimpleT5 Model for Text Simplification in a Limited Corpus2826-2831
  José Monteiro, Micaela Aguiar, Sílvia Araújo
  
• University of Amsterdam at the CLEF 2022 SimpleText Track2832-2844
  Femke Mostert, Ashmita Sampatsing, Mink Spronk, David Rau, Jaap Kamps
  
• HULAT-UC3M at SimpleText@CLEF-2022: Scientific text simplification using BART2845-2851
  Adrián Rubio, Paloma Martínez
  
• NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific Texts2852-2857
  Sourav Saha, Dwaipayan Roy, B Yuvaraj Goud, Chethan S Reddy, Tanmay Basu
  
• Is Using an AI to Simplify a Scientific Text Really Worth It?2858-2861
  Léa Talec-Bernard
  
• CYUT Team2 SimpleText Shared Task Report in CLEF-20222862-2866
  Shih-Hung Wu, Hong-Yi Huang
  

Touché: Argument Retrieval

• Overview of Touché 2022: Argument Retrieval2867-2903
  Alexander Bondarenko, Maik Fröbe, Johannes Kiesel, Shahbaz Syed, Timon Gurcke, Meriem Beloucif, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, Matthias Hagen
  
• Aldo Nadi at Touché 2022: Argument Retrieval for Comparative Questions2904-2918
  Maria Aba, Munzer Azra, Marco Gallo, Odai Mohammad, Ivan Piacere, Giacomo Virginio, Nicola Ferro
  
• SEUPD@CLEF: Team INTSEG on Argument Retrieval for Controversial Questions2919-2932
  Sepide Bahrami, Gnana Prakash Goli, Andrea Pasin, Neemol Rajkumari, Mohammad Muzammil Sohail, Paria Tahan, Nicola Ferro
  
• SEUPD@CLEF: Team Lgtm on Argument Retrieval for Controversial Questions2933-2955
  Manuel Barusco, Gabriele Del Fiume, Riccardo Forzan, Mario Giovanni Peloso, Nicola Rizzetto, Elham Soleymani, Nicola Ferro
  
• SEUPD@CLEF: Team Gamora on Argument Retrieval for Controversial Questions2956-2968
  Alessandro Benetti, Michele De Togni, Giovanni Foti, Ralton Lacini, Andrea Matteazzi, Enrico Sgarbossa, Nicola Ferro
  
• Aramis at Touché 2022: Argument Detection in Pictures using Machine Learning2969-2998
  Jan Braker, Lorenz Heinemann, Tobias Schreieder
  
• Boromir at Touché 2022: Combining Natural Language Processing and Machine Learning Techniques for Image Retrieval for Arguments2999-3017
  Thilo Brummerloh, Miriam Louise Carnot, Shirin Lange, Gregor Pfänder
  
• SEUPD@CLEF: Team 6musk on Argument Retrieval for Controversial Questions by Using Pairs Selection and Query Expansion3018-3031
  Lorenzo Cappellotto, Matteo Lando, Daniel Lupu, Marco Mariotto, Riccardo Rosalen, Nicola Ferro
  
• Retrieving Comparative Arguments using Deep Language Models3032-3040
  Viktoriia Chekalina, Alexander Panchenko
  
• SEUPD@CLEF: Team hextech on Argument Retrieval for Comparative Questions. The importance of adjectives in documents quality evaluation3041-3054
  Alessandro Chimetto, Davide Peressoni, Enrico Sabbatini, Giovanni Tommasin, Marco Varotto, Alessio Zanardelli, Nicola Ferro
  
• Team Bruce Banner at Touché 2022: Argument Retrieval for Controversial Questions3055-3063
  Bernardo C. Moreira, Henrique Lopes Cardoso, Bruno Martins, Fábio Goularte
  
• Stacked Model based Argument Extraction and Stance Detection using Embedded LSTM model3064-3073
  Pavani Rajula, Chia-Chien Hung, Simone Paolo Ponzetto
  
• LEVIRANK: Limited Query Expansion with Voting Integration for Document Retrieval and Ranking3074-3089
  Ashish Rana, Pujit Golchha, Roni Juntunen, Andreea Coajă, Ahmed Elzamarany, Chia-Chien Hung, Simone Paolo Ponzetto
  
• Grimjack at Touché 2022: Axiomatic Re-ranking and Query Reformulation3090-3104
  Jan Heinrich Reimer, Johannes Huck, Alexander Bondarenko
  
• Quality-Aware Argument Re-Ranking for Comparative Questions3105-3114
  Niclas Arnhold, Philipp Rösner, Tobias Xylander
  
• The Pearl Retriever: Two-Stage Retrieval for Pairs of Argumentative Sentences3115-3130
  Sebastian Schmidt, Jonas Probst, Bianca Bartelt, Alexander Hinz
  
• Touché - Task 1 - Team Korg: Finding pairs of argumentative sentences using embeddings3131-3148
  Cuong Vo Ta, Florian Reiner, Immanuel von Detten, Fabian Stöhr
  
• Using BERT to retrieve relevant and argumentative sentence pairs3149-3163
  Nils Wenzlitschke, Pia Sülzle
  
• Similar but Different: Simple Re-ranking Approaches for Argument Retrieval3164-3177
  Jerome Würf