Table of Contents

• Preface [doi]
• Overview of ARQMath-3 (2022): Third CLEF Lab on Answer Retrieval for Questions on Math (Working Notes Version)Behrooz Mansouri, Vít Novotný, Anurag Agarwal, Douglas W. Oard, Richard Zanibbi. 1-27 [doi]
• Diverse Semantics Representation is KingMartin Geletka, Vojtech Kalivoda, Michal Stefánik, Marek Toma, Petr Sojka. 28-39 [doi]
• Dowsing for Answers to Math Questions: Doing Better with LessAndrew Kane, Yin Ki Ng, Frank Wm. Tompa. 40-62 [doi]
• Expanding Spatial Regions and Incorporating IDF for PHOC-Based Math Formula Retrieval at ARQMath-3Matt Langsenkamp, Behrooz Mansouri, Richard Zanibbi. 63-82 [doi]
• DPRL Systems in the CLEF 2022 ARQMath Lab: Introducing MathAMR for Math-Aware SearchBehrooz Mansouri, Douglas W. Oard, Richard Zanibbi. 83-103 [doi]
• Combining Sparse and Dense Information RetrievalVít Novotný, Michal Stefánik. 104-118 [doi]
• Transformer-Encoder and Decoder Models for Questions on MathAnja Reusch, Maik Thiele, Wolfgang Lehner. 119-137 [doi]
• Formula Retrieval Using Structural SimilaritySandip Sarkar, Dipankar Das 0001, Partha Pakray, David Pinto 0001. 138-146 [doi]
• Applying Structural and Dense Semantic Matching for the ARQMath Lab 2022, CLEFWei Zhong, Yuqing Xie 0001, Jimmy Lin. 147-170 [doi]
• Overview of BioASQ Tasks 10a, 10b and Synergy10 in CLEF2022Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Georgios Paliouras. 171-178 [doi]
• Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resourcesAntonio Miranda-Escalada, Luis Gascó, Salvador Lima-López, Eulàlia Farré-Maduell, Darryl Estrada, Anastasios Nentidis, Anastasia Krithara, Georgios Katsimpras, Georgios Paliouras, Martin Krallinger. 179-203 [doi]
• Deep Learning solutions based on fixed contextualized embeddings from PubMedBERT on BioASQ 10b and traditional IR in SynergyTiago Almeida, André Pinho, Rodrigo Pereira, Sérgio Matos. 204-221 [doi]
• Exploring Biomedical Question Answering with BioM-Transformers At BioASQ10B challenge: Findings and TechniquesSultan Alrowili, K. Vijay-Shanker. 222-234 [doi]
• DIAGÑOZA: a Natural Language Processing Tool for Automatic Annotation of Clinical Free Text with SNOMED-CTMatic Bernik, Robert Tovornik, Borut Fabjan, Luis Marco-Ruiz. 235-243 [doi]
• HPI-DHC @ BioASQ DisTEMIST: Spanish Biomedical Entity Linking with Pre-trained Transformers and Cross-lingual Candidate RetrievalFlorian Borchert, Matthieu-P. Schapranow. 244-258 [doi]
• A Simple Terminology-Based Approach to Clinical Entity RecognitionJosé M. Castaño, Laura Gambarte, Carlos Otero, Daniel Luna. 259-264 [doi]
• SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentionsMariia Chizhikova, Jaime Collado-Montañez, Pilar López-Úbeda, Manuel Carlos Díaz-Galiano, Luis Alfonso Ureña López, María-Teresa Martín Valdivia. 265-273 [doi]
• LaRSA at BioASQ 10b: classical and novel approaches for biomedical document retrieval and question answeringZakaria Kaddari, Toumi Bouchentouf. 274-280 [doi]
• Zero-shot Hybrid Retrieval and Reranking Models for Biomedical LiteratureJing Lu, Ji Ma, Keith B. Hall. 281-290 [doi]
• BioTABQA: Instruction Learning for Biomedical Table Question AnsweringMan Luo, Sharad Saxena, Swaroop Mishra, Mihir Parmar, Chitta Baral. 291-304 [doi]
• Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question AnsweringDiego Mollá. 305-314 [doi]
• Biomedical Spanish Language Models for entity recognition and linking at BioASQ DisTEMISTVincenzo Moscato, Marco Postiglione, Giancarlo Sperlì. 315-324 [doi]
• Unicage at DISTEMIST - Named Entity Recognition system using only Bash and Unicage toolsAndré Neves. 325-334 [doi]
• ELECTROLBERT: Combining Replaced Token Detection and Sentence Order PredictionMartin Reczko. 335-340 [doi]
• Clinical Named Entity Recognition and Linking using BERT in Combination with Spanish Medical EmbeddingsJavier Reyes-Aguillón, Rodrigo del Moral, Orlando Ramos Flores, Helena Gómez-Adorno, Gemma Bel Enguix. 341-349 [doi]
• mBERT and Simple Post-Processing: A Baseline for Disease Mention Detection in SpanishAntonio Tamayo, Diego A. Burgos, Alexander F. Gelbukh. 350-356 [doi]
• NCU-IISR/AS-GIS: Using BERTScore and Snippet Score to Improve the Performance of Pretrained Language Model in BioASQ 10b Phase BHao-Hsuan Ting, Yu Zhang, Jen-Chieh Han, Richard Tzong-Han Tsai. 357-367 [doi]
• Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in TweetsPreslav Nakov, Alberto Barrón-Cedeño, Giovanni Da San Martino, Firoj Alam, Rubén Míguez, Tommaso Caselli, Mücahid Kutlu, Wajdi Zaghouani, Chengkai Li, Shaden Shaar, Hamdy Mubarak, Alex Nikolov, Yavuz Selim Kartal. 368-392 [doi]
• Overview of the CLEF-2022 CheckThat! Lab Task 2 on Detecting Previously Fact-Checked ClaimsPreslav Nakov, Giovanni Da San Martino, Firoj Alam, Shaden Shaar, Hamdy Mubarak, Nikolay Babulkov. 393-403 [doi]
• Overview of the CLEF-2022 CheckThat! Lab: Task 3 on Fake News DetectionJuliane Köhler, Gautam Kishore Shahi, Julia Maria Struß, Michael Wiegand, Melanie Siegel, Thomas Mandl 0001, Mina Schütz. 404-421 [doi]
• PoliMi-FlatEarthers at CheckThat!-2022: GPT-3 applied to claim detectionStefano Agresti, S. Amin Hashemian, Mark J. Carman. 422-427 [doi]
• SCUoL at CheckThat!-2022: Fake News Detection Using Transformer-Based ModelsSaud Althabiti, Mohammad Ammar Alsalka, Eric Atwell. 428-433 [doi]
• CIC at CheckThat!-2022: Multi-class and Cross-lingual Fake News DetectionMuhammad Arif, Atnafu Lambebo Tonja, Iqra Ameer, Olga Kolesnikova, Alexander F. Gelbukh, Grigori Sidorov, Abdul Gafar Manuel Meque. 434-443 [doi]
• CODE at CheckThat!-2022: Multi-class fake news detection of news articles with BERTOlivier Blanc, Albert Pritzkau, Ulrich Schade, Michaela Geierhos. 444-455 [doi]
• Zorros at CheckThat!-2022: Ensemble Model for Identifying Relevant Claims in TweetsNicu Buliga, Madalina Raschip. 456-467 [doi]
• NUS-IDS at CheckThat!2022: Identifying Check-worthiness of Tweets using CheckthaT5Mingzhe Du, Sujatha Das Gollapalli, See-Kiong Ng. 468-477 [doi]
• TOBB ETU at CheckThat!-2022: Detecting Attention-Worthy and Harmful Tweets and Check-Worthy ClaimsAhmet Bahadir Eyuboglu, Mustafa Bora Arslan, Ekrem Sonmezer, Mucahid Kutlu. 478-491 [doi]
• Fraunhofer SIT at CheckThat!-2022: Ensemble Similarity Estimation for Finding Previously Fact-Checked ClaimsRaphael Antonius Frick, Inna Vogel. 492-499 [doi]
• Fraunhofer SIT at CheckThat!-2022: Semi-Supervised Ensemble Classification for Detecting Check-Worthy TweetsRaphael Antonius Frick, Inna Vogel, Isabella Nunes Grieser. 500-510 [doi]
• SimBa at CheckThat!-2022: Lexical and Semantic Similarity Based Detection of Verified Claims in an Unsupervised and Supervised WayAlica Hövelmeyer, Katarina Boland, Stefan Dietze. 511-531 [doi]
• RUB-DFL at CheckThat!-2022: Transformer Models and Linguistic Features for Identifying Relevant ClaimsZehra Melce Hüsünbeyi, Oliver Deck, Tatjana Scheffler. 532-545 [doi]
• VTU_BGM at Check That!-2022: An Autoregressive Encoding Model for Detecting Check-worthy ClaimsSanjana Kavatagi, Rashmi Rachh, Madhura Mulimani. 546-553 [doi]
• Text_Minor at CheckThat!-2022: Fake News Article Detection Using RoBERTSujit Kumar, Gaurav Kumar, Sanasam Ranbir Singh. 554-563 [doi]
• BUM at CheckThat!-2022: A Composite Deep Learning Approach to Fake News Detection using Evidence RetrievalDavid La Barbera, Kevin Roitero, Joel Mackenzie, Damiano Spina, Gianluca Demartini, Stefano Mizzaro. 564-572 [doi]
• COURAGE at CheckThat!-2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRAFrancesco Lomonaco, Gregor Donabauer, Marco Siino. 573-583 [doi]
• FoSIL at CheckThat!-2022: Using Human Behaviour-Based Optimization for Text ClassificationAndy Ludwig, Jenny Felser, Jian Xi, Dirk Labudde, Michael Spranger. 584-594 [doi]
• NLPIR-UNED at CheckThat!-2022: Ensemble of Classifiers for Fake News DetectionJuan R. Martinez-Rico, Juan Martínez-Romo, Lourdes Araujo. 595-605 [doi]
• CoulterOzler at CheckThat!-2022: Detecting fake news with transformersKadir Bulut Özler, Riah Coulter. 606-615 [doi]
• HBDCI at CheckThat!-2022: Fake News Detection Using a Combination of stylometric Features and Deep LearningClaudia Porto Capetillo, Diego Lecuona-Gómez, Helena Gómez-Adorno, Ignacio Arroyo-Fernández, Jair Neri-Chávez. 616-628 [doi]
• NLytics at CheckThat!-2022: Hierarchical multi-class fake news detection of news articles exploiting the topic structureAlbert Pritzkau, Olivier Blanc, Michaela Geierhos, Ulrich Schade. 629-648 [doi]
• NITK-IT_NLP at CheckThat!-2022: Window based approach for Fake News Detection using transformersHariharan RamakrishnaIyer LekshmiAmmal, Anand Kumar Madasamy. 649-655 [doi]
• AI Rational at CheckThat!-2022: Using transformer models for tweet classificationAleksandar Savchev. 656-659 [doi]
• AIT_FHSTP at CheckThat!-2022: Cross-Lingual Fake News Detection with a Large Pre-Trained TransformerMina Schütz, Jaqueline Böck, Medina Andresel, Armin Kirchknopf, Daria Liakhovets, Djordje Slijepcevic, Alexander Schindler. 660-670 [doi]
• RIET Lab at CheckThat!-2022: Improving Decoder based Re-ranking for Claim MatchingMichael Shliselberg, Shiri Dori-Hacohen. 671-678 [doi]
• Asatya at CheckThat!-2022: Multimodal BERT for Identifying Claims in TweetsManan Suri, Prajeet Katari, Saumay Dudeja. 679-693 [doi]
• iCompass at CLEF2022 CheckThat! Lab: Combining Deep Language Models for Fake News DetectionBilel Taboubi, Mohamed Aziz Ben Nessir, Hatem Haddad. 694-701 [doi]
• iCompass at CheckThat!-2022: ARBERT and AraBERT for Arabic Checkworthy Tweet IdentificationBilel Taboubi, Mohamed Aziz Ben Nessir, Hatem Haddad. 702-709 [doi]
• Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet TextPrerona Tarannum, Md. Arid Hasan, Firoj Alam, Sheak Rashed Haider Noori. 710-721 [doi]
• ARC-NLP at CheckThat!-2022: Contradiction for Harmful Tweet DetectionCagri Toraman, Oguzhan Ozcelik, Furkan Sahinuç, Umitcan Sahin. 722-739 [doi]
• ur-iw-hnt at CheckThat!-2022: Cross-lingual Text Summarization for Fake News DetectionHoai-Nam Tran, Udo Kruschwitz. 740-748 [doi]
• Awakened at CheckThat!-2022: Fake News Detection using BiLSTM and Sentence TransformerCiprian-Octavian Truica, Elena Simona Apostol, Adrian Paschke. 749-757 [doi]
• Extended Overview of ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical PatentsYuan Li, Biaoyan Fang, Jiayuan He 0002, 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 T. McInnes, Timothy Baldwin, Karin Verspoor. 758-781 [doi]
• Context aware Named Entity Recognition and Relation Extraction with Domain-specific language modelYoungrok Jang, Hosung Song, Junho Lee, Gyeonghun Kim, Yireun Kim, Stanley Jungkyu Choi, Honglak Lee, Kyunghoon Bae. 782-796 [doi]
• HUKB at ChEMU 2022 Task 1: Expression-Level Information ExtractionKojiro Machi, Masaharu Yoshioka. 797-807 [doi]
• NLPatVCU: CLEF 2022 ChEMU Shared Task System DescriptionDarshini Mahendran, Christina Tang, Bridget T. McInnes. 808-820 [doi]
• Overview of eRisk 2022: Early Risk Prediction on the Internet (Extended Overview)Javier Parapar, Patricia Martín-Rodilla, David E. Losada, Fabio Crestani. 821-850 [doi]
• An End-to-End Set Transformer for User-Level Classification of Depression and Gambling DisorderAna-Maria Bucur, Adrian Cosma, Liviu P. Dinu, Paolo Rosso. 851-863 [doi]
• UNED-MED at eRisk 2022: depression detection with TF-IDF, linguistic features and EmbeddingsElena Campillo Ageitos, Juan Martínez-Romo, Lourdes Araujo. 864-874 [doi]
• Early detection of depression using BERT and DeBERTaSreegeethi Devaguptam, Thanmai Kogatam, Nishka Kotian, Anand Kumar M. 875-882 [doi]
• CLEF eRisk 2022: Detecting Early Signs of Pathological Gambling using ML and DL models with dataset chunkingTudor-Andrei Dumitrascu. 883-893 [doi]
• UNED-NLP at eRisk 2022: Analyzing gambling disorders in Social Media using Approximate Nearest NeighborsHermenegildo Fabregat, Andrés Duque, Lourdes Araujo, Juan Martínez-Romo. 894-904 [doi]
• Early risk detection of mental illnesses using various types of textual featuresRodrigo Ferreira, Alina Trifan, José Luís Oliveira. 905-920 [doi]
• Sunday Rockers at eRisk 2022: Early Detection of DepressionRaluca-Andreea Gînga, Andrei-Alexandru Manea, Bogdan-Mihai Dobre. 921-935 [doi]
• Measuring the Severity of the Signs of Eating Disorders Using Similarity-Based ModelsSeyed Habib Hosseini Saravani, Lancelot Normand, Diego Maupomé, Fanny Rancourt, Thomas Soulas, Sara Besharati, Anaelle Normand, Sébastien Mosser 0001, Marie-Jean Meurs. 936-946 [doi]
• UNSL at eRisk 2022: Decision policies with history for early classificationJuan Martín Loyola, Horacio Thompson, Sergio Burdisso, Marcelo Errecalde. 947-960 [doi]
• SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language ProcessingAlba María Mármol-Romero, Salud María Jiménez Zafra, Flor Miriam Plaza del Arco, M. Dolores Molina-González, María-Teresa Martín Valdivia, Arturo Montejo Ráez. 961-971 [doi]
• 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 MediaHarshvardhan Srivastava, Lijin N. S, Sruthi S, Tanmay Basu. 972-986 [doi]
• ZHAW at eRisk 2022: Predicting Signs of Pathological Gambling - GloVe for Snowy DaysSamuel Stalder, Erman Zankov. 987-994 [doi]
• LauSAn at eRisk 2022: Simply and Effectively Optimizing Text Classification for Early DetectionAndreas Säuberli, Sooyeon Cho, Laura Stahlhut. 995-1004 [doi]
• Early detection of depression with linear models using hand-crafted and contextual featuresIlija Tavchioski, Blaz Skrlj, Senja Pollak, Boshko Koloski. 1005-1013 [doi]
• CYUT at eRisk 2022: Early Detection of Depression Based-on Concatenating Representation of Multiple Hidden Layers of RoBERTa ModelShih-Hung Wu, Zhao-Jun Qiu. 1014-1025 [doi]
• TUA1 at eRisk 2022: Exploring Affective Memories for Early Detection of DepressionXin Kang, Rongyu Dou, Haitao Yu 0003. 1026-1037 [doi]
• Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical DocumentsMaud Ehrmann, Matteo Romanello, Sven Najem-Meyer, Antoine Doucet, Simon Clematide. 1038-1063 [doi]
• Knowledge-based Contexts for Historical Named Entity Recognition & LinkingEmanuela Boros, Carlos-Emiliano González-Gallardo, Edward Giamphy, Ahmed Hamdi, Jose G. Moreno 0001, Antoine Doucet. 1064-1078 [doi]
• Entity Linking in Multilingual Newspapers and Classical Commentaries with BERTKai Labusch, Clemens Neudecker. 1079-1089 [doi]
• Exploring Transformers for Multilingual Historical Named Entity RecognitionAnja Ryser, Quynh-Anh Nguyen, Niclas Bodenmann, Shih-Yun Chen. 1090-1108 [doi]
• hmBERT: Historical Multilingual Language Models for Named Entity RecognitionStefan Schweter, Luisa März, Katharina Schmid, Erion Çano. 1109-1129 [doi]
• Overview of iDPP@CLEF 2022: The Intelligent Disease Progression Prediction ChallengeAlessandro Guazzo, Isotta Trescato, Enrico Longato, Enidia Hazizaj, Dennis Dosso, Guglielmo Faggioli, Giorgio Maria Di Nunzio, Gianmaria Silvello, Martina Vettoretti, Erica Tavazzi, Chiara Roversi, Piero Fariselli, Sara C. Madeira, Mamede de Carvalho, Marta Gromicho, Adriano Chiò, Umberto Manera, Arianna Dagliati, Giovanni Birolo, Helena Aidos, Barbara Di Camillo, Nicola Ferro 0001. 1130-1210 [doi]
• Hierarchical Modelling for ALS Prognosis: Predicting the Progression Towards Critical EventsRuben Branco, Diogo F. Soares, Andreia S. Martins, Eleonora Auletta, Eduardo N. Castanho, Susana Nunes, Filipa Serrano, Rita Torres Sousa, Catia Pesquita, Sara C. Madeira, Helena Aidos. 1211-1227 [doi]
• Evaluation of XAI on ALS 6-months mortality predictionTommaso Mario Buonocore, Giovanna Nicora, Arianna Dagliati, Enea Parimbelli. 1228-1235 [doi]
• Predicting the Risk of & Time to Impairment for ALS patientsAidan Mannion, Thierry Chevalier, Didier Schwab, Lorraine Goeuriot. 1236-1255 [doi]
• Explaining Artificial Intelligence Predictions of Disease Progression with Semantic SimilaritySusana Nunes, Rita Torres Sousa, Filipa Serrano, Ruben Branco, Diogo F. Soares, Andreia S. Martins, Eleonora Auletta, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos, Catia Pesquita. 1256-1268 [doi]
• Multi-Event Survival Prediction for Amyotrophic Lateral SclerosisCorrado Pancotti, Giovanni Birolo, Tiziana Sanavia, Cesare Rollo, Piero Fariselli. 1269-1276 [doi]
• Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease ProgressionIsotta Trescato, Alessandro Guazzo, Enrico Longato, Enidia Hazizaj, Chiara Roversi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo. 1277-1293 [doi]
• Overview of ImageCLEFmedical 2022 - Caption Prediction and Concept DetectionJohannes Rückert, Asma Ben Abacha, Alba Garcia Seco de Herrera, Louise Bloch, Raphael Brüngel, Ahmad Idrissi-Yaghir, Henning Schäfer, Henning Müller, Christoph M. Friedrich. 1294-1307 [doi]
• Overview of ImageCLEFtuberculosis 2022 - CT-based Cavern Detection and ReportSerge Kozlovski, Yashin Dicente Cid, Vassili Kovalev, Henning Müller. 1308-1317 [doi]
• ImageCLEFcoral task: Coral reef image annotation and localisationJon Chamberlain, Alba García Seco de Herrera, Antonio Campello, Adrian F. Clark. 1318-1328 [doi]
• Overview of the ImageCLEF 2022 Aware TaskAdrian Popescu 0001, Jérôme Deshayes-Chossart, Hugo Schindler, Bogdan Ionescu. 1329-1338 [doi]
• Overview of ImageCLEFfusion 2022 Task - Ensembling Methods for Media Interestingness Prediction and Result DiversificationLiviu-Daniel Stefan, Mihai Gabriel Constantin, Mihai Dogariu, Bogdan Ionescu. 1339-1346 [doi]
• Caverns Detection and Caverns Report in Tuberculosis: lesion detection based on image using YOLO-V3 and median based multi-label multi-class classification using SRGANTetsuya Asakawa, Riku Tsuneda, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono. 1347-1354 [doi]
• AUEB NLP Group at ImageCLEFmed Caption 2022Foivos Charalampakos, Giorgos Zachariadis, John Pavlopoulos, Vasilis Karatzas, Christoforos Trakas, Ion Androutsopoulos. 1355-1373 [doi]
• AIMultimediaLab at ImageCLEFfusion 2022: DeepFusion Methods for Ensembling in Diverse ScenariosMihai Gabriel Constantin, Liviu-Daniel Stefan, Mihai Dogariu, Bogdan Ionescu. 1374-1380 [doi]
• CMRE-UoG team at ImageCLEFmedical Caption 2022: Concept Detection and Image CaptioningFrancesco Dalla Serra, Fani Deligianni, Jeffrey Dalton 0001, Alison Q. O'Neil. 1381-1390 [doi]
• ImageCLEFmedical Caption Task, Concept Detection, Finding Duplicates, SDVA-UCSD ApproachAmilcare Gentili. 1391-1395 [doi]
• Dual Convolutional Neural Networks and Regression model based Coral Reef Annotation and LocalizationRohit Raj Gunti, Abebe Rorissa. 1396-1412 [doi]
• IUST_NLPLAB at ImageCLEFmedical Caption Tasks 2022Malihe Hajihosseini, Yasaman Lotfollahi, Melika Nobakhtian, Mohammad Mahdi Javid, Fateme Omidi, Sauleh Eetemadi. 1413-1434 [doi]
• A Fusion Approach for Web Search Result Diversification using Machine Learning AlgorithmsLekshmi Kalinathan, Prabavathy Balasundaram, Yogesh Munees E, Siddharth S, Shrijith Mr, Shrikeshavinee Ramachandran, Shruti Sriram, Ramdhanush Venkatakrishnan. 1435-1442 [doi]
• Monitoring Coral Reefs Using Faster R-CNNFelix Kerlin, Kirill Bogomasov, Stefan Conrad 0001. 1443-1454 [doi]
• CSIRO at ImageCLEFmedical Caption 2022Léo Lebrat, Aaron Nicolson, Rodrigo Santa Cruz, Gregg Belous, Bevan Koopman, Jason Dowling. 1455-1473 [doi]
• Semi-supervised Multi-Label Classification with 3D CBAM Resnet for Tuberculosis Cavern ReportXing Lu, an Yan, Eric Y. Chang, Chun-Nan Hsu, Julian J. McAuley, Jiang Du, Amilcare Gentili. 1474-1479 [doi]
• Polimi-ImageClef Group at ImageCLEFmedical Caption task 2022Seyyed Ali Mir Ghayyomnia, Kai de Gast, Mark J. Carman. 1480-1486 [doi]
• NeuralDynamicsLab at ImageCLEFmedical 2022Georgios Moschovis, Erik Fransén. 1487-1504 [doi]
• SSN MLRG at ImageCLEFmedical Caption 2022: Medical Concept Detection and Caption Prediction using Transfer Learning and Transformer based Learning ApproachesSheerin Sitara Noor Mohamed, Kavitha Srinivasan. 1505-1515 [doi]
• SSN CSE at ImageCLEFaware 2022: Contextual Job Search Feedback Score based on Photographic Profile using a Random Forest Regression TechniqueAarthi Nunna, Aravind Kannan Rathinasapabathi, Chirag Bheemaiah P. K, Kavitha Srinivasan. 1516-1524 [doi]
• CS_Morgan at ImageCLEFmedical 2022 Caption Task: Deep Learning Based Multi-Label Classification and Transformers for Concept Detection & Caption PredictionMd Mahmudur Rahman 0003, Oyebisi Layode. 1525-1534 [doi]
• Detecting Concepts and Generating Captions from Medical Images: Contributions of the VCMI Team to ImageCLEFmedical 2022 CaptionIsabel Rio-Torto, Cristiano Patrício, Helena Montenegro, Tiago Gonçalves. 1535-1553 [doi]
• SSN MLRG at ImageCLEF 2022 Tuberculosis: Caverns Report using 3D CNN and Uniformizing TechniquesDheepak S, Kavitha Srinivasan, Raghuraman G. 1554-1561 [doi]
• A Late Fusion Framework with Multiple Optimization Methods for Media InterestingnessMaria Shoukat, Khubaib Ahmad, Naina Said, Nasir Ahmad, Mohammed Hasanuzzaman, Kashif Ahmad. 1562-1571 [doi]
• Automated Classification of Lung Tuberculosis Using 3D Deep Convolutional Neural NetworksSushaanth Srinivasan, Sharvesh Shankar, Nitheesh Kumar N, Sabarivasan Velayutham, Thejas N, Vikash Anand N, Lekshmi Kalinathan, Prabavathy Balasundaram. 1572-1579 [doi]
• Ensembled Approach for Web Search Result Diversification Using Neural NetworksShreya Sriram, Madhuri Mahalingam, Sarah Aymen Naseer, Shajith Hameed, Rahul Rajagopalan, Sai Shashaank R, Lekshmi Kalinathan, Prabavathy Balasundaram. 1580-1589 [doi]
• Multi Regressor Based User Rating Predictor for ImageCLEF Aware 2022Aarthi Suresh Kumar, Anirudh Anand, Jeet Golecha, Karthik Raja Anandan, Bhuvana Jayaraman, Mirnalinee T. T. 1590-1595 [doi]
• Kdelab at ImageCLEFmedical 2022 Caption Prediction TaskRiku Tsuneda, Tetsuya Asakawa, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono. 1596-1607 [doi]
• Kdelab at ImageCLEFmedical 2022: Medical Concept Detection with Image Retrieval and Code EnsembleRiku Tsuneda, Tetsuya Asakawa, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono. 1608-1618 [doi]
• ImageSem Group at ImageCLEFmedical Caption 2022 task: Generating Medical Image Descriptions based on Vision-Language Pre-trainingXuwen Wang, Jiao Li. 1619-1625 [doi]
• CSIRO at the ImageCLEFmedical 2022 Tuberculosis Caverns Detection Challenge: A 2D and 3D Deep Learning Detection Network ApproachBowen Xin, Hang Min, Ashley G. Gillman, Bevan Koopman, Jason Dowling, Aaron Nicolson. 1626-1640 [doi]
• Overview of the CLEF 2022 JOKER Task 1: Classify and Explain Instances of WordplayLiana Ermakova, Fabio Regattin, Tristan Miller, Anne-Gwenn Bosser, Sílvia Araújo, Claudine Borg, Gaëlle Le Corre, Julien Boccou, Albin Digue, Aurianne Damoy, Paul Campen, Orlane Puchalski. 1641-1665 [doi]
• Overview of the CLEF 2022 JOKER Task 2: Translate Wordplay in Named EntitiesLiana Ermakova, Tristan Miller, Julien Boccou, Albin Digue, Aurianne Damoy, Paul Campen. 1666-1680 [doi]
• Overview of the CLEF 2022 JOKER Task 3: Pun Translation from English into FrenchLiana Ermakova, Fabio Regattin, Tristan Miller, Anne-Gwenn Bosser, Claudine Borg, Benoît Jeanjean, Élise Mathurin, Gaëlle Le Corre, Radia Hannachi, Sílvia Araújo, Julien Boccou, Albin Digue, Aurianne Damoy. 1681-1700 [doi]
• Wordplay location and interpretation with deep learning methodsHakima Arroubat. 1701-1705 [doi]
• Using 'punning schemes' as a template for translating wordplayJulien Boccou. 1706-1718 [doi]
• Poetic or Humorous Text Generation: Jam event at PFIA2022Anne-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. 1719-1726 [doi]
• A history of classification and JokeR's reachPaul Campen, Albin Digue. 1727-1734 [doi]
• Translation strategies: adaptation and equivalence - Joker contestAurianne Damoy. 1735-1740 [doi]
• Translating, transcribing, transmitting and transcending a pun: why playing with words is far from being punless/pointlessCharlotte Daniel, Noémie Vandenborre. 1741-1749 [doi]
• A translation-oriented categorisation of wordplaysMichel Delarche. 1750-1755 [doi]
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• Automatic translation of wordplayAlbin Digue, Paul Campen. 1774-1786 [doi]
• Using machine learning to classify and interpret wordplayAygyul Epimakhova. 1787-1792 [doi]
• Humorous Wordplay Generation in FrenchLoic Glemarec, Anne-Gwenn Bosser, Julien Boccou, Liana Ermakova. 1793-1806 [doi]
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• How good can an automatic translation of Pokémon names be?Léa Talec-Bernard. 1846-1848 [doi]
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• Overview of PlantCLEF 2022: Image-based plant identification at global scaleHervé Goëau, Pierre Bonnet, Alexis Joly. 1916-1928 [doi]
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• Few-shot Long-Tailed Bird Audio RecognitionMarcos V. Conde, Ui-Jin Choi. 2036-2046 [doi]
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• Does Closed-Set Training Generalize to Open-Set Recognition?Fan Gao, Zining Chen, Weiqiu Wang, Yinan Song, Fei Su, Zhicheng Zhao, Hong Chen. 2063-2077 [doi]
• Plant and Animal Species Prediction at Certain Locations with Convolutional Neural NetworksJuntao Jiang. 2078-2086 [doi]
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• Plant Species Identification using Probability Tree Approach of Deep Learning ModelsKarun Anantharaman, Krishnakumar Divyasri, Sella Veluswami Jansi Rani. 2094-2102 [doi]
• Block Label Swap for Species Distribution ModellingBenjamin Kellenberger, Devis Tuia. 2103-2114 [doi]
• Bird Species Classification: One Step at a TimeSidhart Krishnan, Priya Khandelwal, Rhythm Garg. 2115-2122 [doi]
• Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural NetworksCésar Leblanc, Alexis Joly, Titouan Lorieul, Maximilien Servajean, Pierre Bonnet. 2123-2150 [doi]
• Dealing with Class Imbalance in Bird Sound ClassificationEduard Martynov, Yuuichiroh Uematsu. 2151-2158 [doi]
• Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022Anthony Miyaguchi, Jiangyue Yu, Bryan Cheungvivatpant, Dakota Dudley, Aniketh Swain. 2159-2167 [doi]
• Image-based plant identification with taxonomy aware architectureJack Min Ong, Sze Jue Yang, Kam Woh Ng, Chee Seng Chan. 2168-2174 [doi]
• Deep Learning and Gradient Boosting Ensembles for Classification of Snake SpeciesMirunalini Palaniappan, Karthik Desingu, Haricharan Bharathi, Eeswara Anvesh Chodisetty, Anirudh Bhaskar. 2175-2188 [doi]
• TUC Media Computing at BirdCLEF 2022: Strategies in identifying bird sounds in a complex acoustic environmentsArunodhayan Sampath Kumar, Danny Kowerko. 2189-2198 [doi]
• When Large Kernel Meets Vision Transformer: A Solution for SnakeCLEF & FungiCLEFYang Shen, Xuhao Sun, Zijian Zhu. 2199-2211 [doi]
• Convolution Neural Network Fine-tuning for Plant and Animal Species Distribution ModellingMélisande Teng, Sal Elkafrawy. 2212-2218 [doi]
• Transformer-based Fine-Grained Fungi Classification in an Open-Set ScenarioStefan Wolf, Jürgen Beyerer. 2219-2226 [doi]
• 1st Place Solution for FungiCLEF 2022 Competition: Fine-grained Open-set Fungi RecognitionZihua Xiong, Yumeng Ruan, Yifei Hu, Yue Zhang, Yuke Zhu, Sheng Guo 0005, Bing Han. 2227-2237 [doi]
• Transfer Learning with Self-Supervised Vision Transformer for Large-Scale Plant IdentificationMingle Xu, Sook Yoon, Yongchae Jeong, Jaesu Lee, Dong-Sun Park. 2238-2252 [doi]
• Solution for SnakeCLEF 2022 by Tackling Long-tailed CategorizationLingfeng Yang, Xiang Li, Renjie Song, Kexin Zhu, Gang Li. 2253-2261 [doi]
• Efficient Model Integration for Snake ClassificationJun Yu, Hao Chang, Zhongpeng Cai, Guochen Xie, Liwen Zhang, Keda Lu, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang 0002. 2262-2274 [doi]
• Bag of Tricks and a Strong Baseline for FGVCJun Yu 0001, Hao Chang, Keda Lu, Guochen Xie, Liwen Zhang, Zhongpeng Cai, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang 0002. 2275-2290 [doi]
• Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022Cheng Zou, Furong Xu, Meng Wang, Wen Li, Yuan Cheng. 2291-2300 [doi]
• Overview of the Authorship Verification Task at PAN 2022Efstathios Stamatatos, Mike Kestemont, Krzysztof Kredens, Piotr Pezik, Annina Heini, Janek Bevendorff, Benno Stein 0001, Martin Potthast. 2301-2313 [doi]
• Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO). Overview for PAN at CLEF 2022Reynier Ortega Bueno, Berta Chulvi, Francisco Rangel, Paolo Rosso, Elisabetta Fersini. 2314-2343 [doi]
• Overview of the Style Change Detection Task at PAN 2022Eva Zangerle, Maximilian Mayerl, Martin Potthast, Benno Stein 0001. 2344-2356 [doi]
• Ensemble-Based Clustering for Writing Style Change Detection in Multi-Authored Textual DocumentsShams Alshamasi, Mohamed Menai. 2357-2374 [doi]
• Style Change Detection using Discourse MarkersFaisal Alvi, Hasan Algafri, Naif Alqahtani. 2375-2380 [doi]
• User profiling: voting schemeMaría Fernanda Artigas Herold, Daniel Castro-Castro. 2381-2390 [doi]
• Profiling Irony and Stereotype Spreaders with Encoding Dependency Information using Graph Convolutional NetworkHamed Babaei Giglou, Mostafa Rahgouy, Ali Rahmati, Taher Rahgooy, Cheryl D. Seals. 2391-2401 [doi]
• CIC@PAN: Simplifying Irony Profiling using Twitter DataSabur Butt, Fazlourrahman Balouchzahi, Grigori Sidorov, Alexander F. Gelbukh. 2402-2410 [doi]
• A Multi-Model Voting Ensemble Classifier based on BERT for Profiling Irony and Stereotype Spreaders on TwitterHaojie Cao, Zhongyuan Han, Zhenwei Mo, Zengyao Li, Ziwei Xiao, Zijian Li 0006, Leilei Kong. 2411-2415 [doi]
• A content spectral-based analysis for authorship verificationMelesio Crespo-Sanchez, Helena Gómez-Adorno, Ivan López-Arévalo, Edwin Aldana-Bobadilla, Karla Salas-Jimenez, Jorge Cortes-Lopez. 2416-2425 [doi]
• An SVM Ensemble Approach to Detect Irony and Stereotype Spreaders on TwitterDaniele Croce, Domenico Garlisi, Marco Siino. 2426-2432 [doi]
• Irony and Stereotype Spreading Author Profiling on Twitter using Machine Learning: A BERT-TFIDF based ApproachAmit Das, Nilanjana Raychawdhary, Gerry V. Dozier, Cheryl D. Seals. 2433-2444 [doi]
• UMUTeam at IROSTEREO: Profiling Irony and Stereotype spreaders on Twitter combining Linguistic Features with TransformersJosé Antonio García-Díaz, Miguel Ángel Rodríguez-García, Francisco García-Sánchez 0001, Rafael Valencia-García. 2445-2454 [doi]
• An Unorthodox Approach for Style Change DetectionLukas Graner, Paul Ranly. 2455-2466 [doi]
• BERT Sentence Embeddings in different Machine Learning and Deep Learning Models for Author Profiling applied to Irony and Stereotype Spreaders on TwitterClaudia Gómez, Daniel Parres. 2467-2474 [doi]
• Profiling Irony Speech Spreaders on Social Networks Using Deep Cleaning and BERTLeila Hazrati, Alireza Sokhandan, Leili Farzinvash. 2475-2481 [doi]
• Exploiting Affective-based Information for Profiling Ironic Users on TwitterDelia Irazu Hernandez Farias, Manuel Montes-y-Gómez. 2482-2490 [doi]
• Authorship verification Based On Fully Interacted Text SegmentsMingjie Huang, Leilei Kong, Zeyang Peng, Yihui Ye, Zengyao Li, Xinyin Jiang, Zhongyuan Han. 2491-2495 [doi]
• Profiling Irony and Stereotype Spreaders with Language Models and Bayes' TheoremXinting Huang. 2496-2505 [doi]
• UniNE at PAN-CLEF 2022: Profiling Irony and Stereotype Spreaders on TwitterCatherine Ikae. 2506-2514 [doi]
• Lexicon-Based Profiling of Irony and Stereotype SpreadersHyewon Jang. 2515-2525 [doi]
• Style Change Detection: Method Based On Pre-trained Model And Similarity RecognitionXinyin Jiang, Haoliang Qi, Zhijie Zhang, Mingjie Huang. 2526-2531 [doi]
• Different Encoding Approaches for Authorship VerificationStefanos Konstantinou, Jinqiao Li, Angelos Zinonos. 2532-2540 [doi]
• Graph-Based Profile Condensation for Users ProfilingRoberto Labadie Tamayo, Daniel Castro-Castro. 2541-2553 [doi]
• Style Change Detection Based On Bert And Conv1dQidi Lao, Li Ma, Wenyin Yang, Zexian Yang, Dong Yuan, Zhenlin Tan, Langzhang Liang. 2554-2559 [doi]
• Application of BERT in author verification taskZiwang Lei, Haoliang Qi, Han Y, Zeyang Peng, Mingjie Huang. 2560-2564 [doi]
• Ensemble Pre-trained Transformer Models for Writing Style Change DetectionTzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng, Lung-Hao Lee. 2565-2573 [doi]
• A BERT-based model for Profiling Irony and Stereotype Spreaders on TwitterWenbin Lin, Zhongyuan Han, Jinxi Zhang, Zengyao Li, Guiyuan Cao, Jianhong Yu, Leilei Kong. 2574-2577 [doi]
• Profiling Irony and Stereotype Spreaders on Twitter Using TF-IDF and Neural NetworkHaolong Ma, Dingjia Li, Yutong Sun. 2578-2584 [doi]
• Improving Irony and Stereotype Spreaders Detection using Data Augmentation and Convolutional Neural NetworkStefano Mangione, Marco Siino, Giovanni Garbo. 2585-2593 [doi]
• Graph-Based Siamese Network for Authorship VerificationJorge Alfonso Martinez Galicia, Daniel Embarcadero-Ruiz, Alejandro Ríos Orduña, Helena Gómez-Adorno. 2594-2606 [doi]
• Text-to-Text Transformer in Authorship Verification Via Stylistic and Semantical AnalysisMaryam Najafi, Ehsan Tavan. 2607-2616 [doi]
• Use pre-trained models and multi-classifier voting methods to identify the ironic authors on TwitterJian Qin, Leilei Kong, Zhaoqian Huang, Jialin Huang, Yansheng Guo, Mingjie Huang, Zeyang Peng. 2617-2622 [doi]
• Irony &Stereotype Spreader Detection using Random ForestsTiago Filipe Nunes Ribeiro, Yana Nikolaeva Nikolova, Kaja Seraphina Elisa Hano. 2623-2641 [doi]
• Three Style Similarity: sentence-embedding, auxiliary words, punctuationCarlos Alberto Rodríguez-Losada, Daniel Castro-Castro. 2642-2652 [doi]
• Isolated Profile Style RepresentationCarlos Alberto Rodríguez-Losada, Daniel Castro-Castro. 2653-2660 [doi]
• Profiling Irony and Stereotype Spreaders on Twitter: PAN Shared Task (IROSTEREO) 2022Alvaro Rodríguez Sánchez, Martín Barroso Ordóñez. 2661-2665 [doi]
• T100: A modern classic ensemble to profile irony and stereotype spreadersMarco Siino, Ilenia Tinnirello, Marco La Cascia. 2666-2674 [doi]
• Identifying Author Profiles Containing Irony or Spreading Stereotypes with SBERT and EmojisNarjes Tahaei, Harsh Verma, Parsa Bagherzadeh, Farhood Farahnak, Nadia Sheikh, Sabine Bergler. 2675-2681 [doi]
• Profiling irony and stereotype spreaders on Twitter based on term frequency in tweetsDhaval Taunk, Sagar Joshi, Vasudeva Varma. 2682-2686 [doi]
• Identifying Ironic Content Spreaders on Twitter using Psychometrics, Contextual and Ironic Features with Gradient Boosting ClassifierEhsan Tavan, Maryam Najafi, Reza Moradi. 2687-2697 [doi]
• Notebook for PAN at CLEF 2022: Profiling Irony and Stereotype Spreaders on TwitterBin Wang, Hui Ning. 2698-2702 [doi]
• Profiling Irony and Stereotype Spreaders on Twitter with BERTYifan Xu, Hui Ning. 2703-2707 [doi]
• A Intelligent Detection Method for Irony and Stereotype Based on Hybird Neural NetworksZexian Yang, Li Ma, Wenyin Yang, Qidi Lao, Zhenlin Tan. 2708-2713 [doi]
• Authorship Verification Using Convolutional Neural NetworkYihui Ye, Han Y, Zeyang Peng, Mingjie Huang, Leilei Kong, Zhongyuan Han. 2714-2719 [doi]
• BERT-based ironic authors profilingWentao Yu, Benedikt T. Boenninghoff, Dorothea Kolossa. 2720-2733 [doi]
• Analysis of Irony and Stereotype Spreaders Based On Convolutional Neural NetworksDong Yuan, Wenyin Yang, Li Ma, Zhenlin Tan, Qidi Lao. 2734-2738 [doi]
• An Ensemble Machine Learning Classifier for Profiling Irony and Stereotype Spreaders on TwitterZengyao Li, Zhongyuan Han, Mingjie Huang, Leilei Kong. 2739-2745 [doi]
• Irony and Stereotype Spreaders Detection using BERT-large and AutoGulonYuning Zhang, Hui Ning. 2746-2752 [doi]
• Style Change Detection based on PromptZhijie Zhang, Zhongyuan Han, Leilei Kong. 2753-2756 [doi]
• Style Change Detection Based On Bi-LSTM And BertJiayang Zi, Ling Zhou, Zhengyao Liu. 2757-2761 [doi]
• Overview of the CLEF 2022 SimpleText Task 1: Passage Selection for a Simplified SummaryEric SanJuan, Stéphane Huet, Jaap Kamps, Liana Ermakova. 2762-2772 [doi]
• Overview of the CLEF 2022 SimpleText Task 2: Complexity Spotting in Scientific AbstractsLiana Ermakova, Irina Ovchinnikova, Jaap Kamps, Diana Nurbakova, Sílvia Araújo, Radia Hannachi. 2773-2791 [doi]
• Overview of the CLEF 2022 SimpleText Task 3: Query Biased Simplification of Scientific TextsLiana Ermakova, Irina Ovchinnikova, Jaap Kamps, Diana Nurbakova, Sílvia Araújo, Radia Hannachi. 2792-2804 [doi]
• Searching for explanation of difficult scientific termsMajda Ennaciri. 2805-2809 [doi]
• Assembly Models for SimpleText Task 2: Results from Wuhan University Research GroupJianfei Huang, Jin-Mao. 2810-2817 [doi]
• Controllable Sentence Simplification Using Transfer LearningAntonio Menta Garuz, Ana García-Serrano. 2818-2825 [doi]
• Using a Pre-trained SimpleT5 Model for Text Simplification in a Limited CorpusJosé Monteiro, Micaela Aguiar, Sílvia Araújo. 2826-2831 [doi]
• University of Amsterdam at the CLEF 2022 SimpleText TrackFemke Mostert, Ashmita Sampatsing, Mink Spronk, David Rau, Jaap Kamps. 2832-2844 [doi]
• HULAT-UC3M at SimpleText@CLEF-2022: Scientific text simplification using BARTAdrián Rubio, Paloma Martínez. 2845-2851 [doi]
• NLP-IISERB@Simpletext2022: To Explore the Performance of BM25 and Transformer Based Frameworks for Automatic Simplification of Scientific TextsSourav Saha 0003, Dwaipayan Roy, B. Yuvaraj Goud, Chethan S. Reddy, Tanmay Basu. 2852-2857 [doi]
• Is Using an AI to Simplify a Scientific Text Really Worth It?Léa Talec-Bernard. 2858-2861 [doi]
• CYUT Team2 SimpleText Shared Task Report in CLEF-2022Shih-Hung Wu, Hong-Yi Huang. 2862-2866 [doi]
• Overview of Touché 2022: Argument RetrievalAlexander Bondarenko, Maik Fröbe, Johannes Kiesel, Shahbaz Syed, Timon Gurcke, Meriem Beloucif, Alexander Panchenko, Chris Biemann, Benno Stein 0001, Henning Wachsmuth, Martin Potthast, Matthias Hagen. 2867-2903 [doi]
• Aldo Nadi at Touché 2022: Argument Retrieval for Comparative QuestionsMaria Aba, Munzer Azra, Marco Gallo, Odai Mohammad, Ivan Piacere, Giacomo Virginio, Nicola Ferro 0001. 2904-2918 [doi]
• SEUPD@CLEF: Team INTSEG on Argument Retrieval for Controversial QuestionsSepide Bahrami, Gnana Prakash Goli, Andrea Pasin, Neemol Rajkumari, Mohammad Muzammil Sohail, Paria Tahan, Nicola Ferro 0001. 2919-2932 [doi]
• SEUPD@CLEF: Team Lgtm on Argument Retrieval for Controversial QuestionsManuel Barusco, Gabriele Del Fiume, Riccardo Forzan, Mario Giovanni Peloso, Nicola Rizzetto, Elham Soleymani, Nicola Ferro 0001. 2933-2955 [doi]
• SEUPD@CLEF: Team Gamora on Argument Retrieval for Controversial QuestionsAlessandro Benetti, Michele De Togni, Giovanni Foti, Ralton Lacini, Andrea Matteazzi, Enrico Sgarbossa, Nicola Ferro 0001. 2956-2968 [doi]
• Aramis at Touché 2022: Argument Detection in Pictures using Machine LearningJan Braker, Lorenz Heinemann, Tobias Schreieder. 2969-2998 [doi]
• Boromir at Touché 2022: Combining Natural Language Processing and Machine Learning Techniques for Image Retrieval for ArgumentsThilo Brummerloh, Miriam Louise Carnot, Shirin Lange, Gregor Pfänder. 2999-3017 [doi]
• SEUPD@CLEF: Team 6musk on Argument Retrieval for Controversial Questions by Using Pairs Selection and Query ExpansionLorenzo Cappellotto, Matteo Lando, Daniel Lupu, Marco Mariotto, Riccardo Rosalen, Nicola Ferro 0001. 3018-3031 [doi]
• Retrieving Comparative Arguments using Deep Language ModelsViktoria Chekalina, Alexander Panchenko. 3032-3040 [doi]
• SEUPD@CLEF: Team hextech on Argument Retrieval for Comparative Questions. The importance of adjectives in documents quality evaluationAlessandro Chimetto, Davide Peressoni, Enrico Sabbatini, Giovanni Tommasin, Marco Varotto, Alessio Zanardelli, Nicola Ferro 0001. 3041-3054 [doi]
• Team Bruce Banner at Touché 2022: Argument Retrieval for Controversial QuestionsBernardo C. Moreira, Henrique Lopes Cardoso, Bruno Martins 0001, Fábio Goularte. 3055-3063 [doi]
• Stacked Model based Argument Extraction and Stance Detection using Embedded LSTM modelPavani Rajula, Chia-Chien Hung, Simone Paolo Ponzetto. 3064-3073 [doi]
• LEVIRANK: Limited Query Expansion with Voting Integration for Document Retrieval and RankingAshish Rana, Pujit Golchha, Roni Juntunen, Andreea Coaja, Ahmed Elzamarany, Chia-Chien Hung, Simone Paolo Ponzetto. 3074-3089 [doi]
• Grimjack at Touché 2022: Axiomatic Re-ranking and Query ReformulationJan Heinrich Reimer, Johannes Huck, Alexander Bondarenko. 3090-3104 [doi]
• Quality-Aware Argument Re-Ranking for Comparative QuestionsNiclas Arnhold, Philipp Rösner, Tobias Xylander. 3105-3114 [doi]
• The Pearl Retriever: Two-Stage Retrieval for Pairs of Argumentative SentencesSebastian Schmidt, Jonas Probst, Bianca Bartelt, Alexander Hinz. 3115-3130 [doi]
• Touché - Task 1 - Team Korg: Finding pairs of argumentative sentences using embeddingsCuong Vo Ta, Florian Reiner, Immanuel von Detten, Fabian Stöhr. 3131-3148 [doi]
• Using BERT to retrieve relevant and argumentative sentence pairsNils Wenzlitschke, Pia Sülzle. 3149-3163 [doi]
• Similar but Different: Simple Re-ranking Approaches for Argument RetrievalJerome Würf. 3164-3177 [doi]