Table of Contents

• Preface [doi]
• Overview of ARQMath-2 (2021): Second CLEF Lab on Answer Retrieval for Questions on Math (Working Notes Version)Behrooz Mansouri, Richard Zanibbi, Douglas W. Oard, Anurag Agarwal. 1-24 [doi]
• XY-PHOC Symbol Location Embeddings for Math Formula Retrieval and AutocompletionRobin Avenoso, Behrooz Mansouri, Richard Zanibbi. 25-35 [doi]
• BERT-Based Embedding Model for Formula RetrievalPankaj Dadure, Partha Pakray, Sivaji Bandyopadhyay. 36-46 [doi]
• DPRL Systems in the CLEF 2021 ARQMath Lab: Sentence-BERT for Answer Retrieval, Learning-to-Rank for Formula RetrievalBehrooz Mansouri, Douglas W. Oard, Richard Zanibbi. 47-62 [doi]
• Dowsing for Answers to Math Questions: Ongoing Viability of Traditional MathIRYin Ki Ng, Dallas J. Fraser, Besat Kassaie, Frank Wm. Tompa. 63-81 [doi]
• Ensembling Math Information Retrieval Systems: MIRMU and MSM at ARQMath 2021Vít Novotný, Michal Stefánik, Dávid Lupták, Martin Geletka, Petr Zelina, Petr Sojka. 82-106 [doi]
• TU_DBS in the ARQMath Lab 2021, CLEFAnja Reusch, Maik Thiele, Wolfgang Lehner. 107-124 [doi]
• Ranked List Fusion and Re-ranking with Pre-trained Transformers for ARQMath LabShaurya Rohatgi, Jian Wu 0006, C. Lee Giles. 125-132 [doi]
• Approach Zero and Anserini at the CLEF-2021 ARQMath Track: Applying Substructure Search and BM25 on Operator Tree Path TokensWei Zhong, Xinyu Zhang, Ji Xin, Richard Zanibbi, Jimmy Lin. 133-156 [doi]
• Overview of BioASQ Tasks 9a, 9b and Synergy in CLEF2021Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Georgios Paliouras. 157-164 [doi]
• Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trialsLuis Gascó, Anastasios Nentidis, Anastasia Krithara, Darryl Estrada-Zavala, Renato Toshiyuki Murasaki, Elena Primo-Peña, Cristina Bojo-Canales, Georgios Paliouras, Martin Krallinger. 165-187 [doi]
• BioASQ Synergy: A strong and simple baseline rooted in relevance feedbackTiago Almeida, Sérgio Matos. 188-195 [doi]
• Universal Passage Weighting Mecanism (UPWM) in BioASQ 9bTiago Almeida, Sérgio Matos. 196-212 [doi]
• Large Biomedical Question Answering Models with ALBERT and ELECTRASultan Alrowili, Vijay-Shanker. 213-220 [doi]
• Vicomtech at MESINESP2: BERT-based Multi-label Classification Models for Biomedical Text IndexingAitor García Pablos, Naiara Pérez, Montse Cuadros. 221-230 [doi]
• MDS_UNCC Question Answering System for Biomedical Data with Preliminary Error AnalysisSeethalakshmi Gopalakrishnan, Swathi Padithala, Hilmi Demirhan, Wlodek Zadrozny. 231-240 [doi]
• PIDNA at BioASQ MESINESP: Hybrid Semantic Indexing for Biomedical Articles in SpanishYi Huang, Buse Giledereli, Abdullatif Köksal, Arzucan Özgür, Elif Ozkirimli. 241-246 [doi]
• Transformer-based Language Models for Factoid Question Answering at BioASQ9bUrvashi Khanna, Diego Mollá. 247-257 [doi]
• Post-processing BioBERT And Using Voting Methods for Biomedical Question AnsweringMargarida M. Campos, Francisco M. Couto. 258-273 [doi]
• Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 QuestionsDiego Molla, Urvashi Khanna, Dima Galat, Vincent Nguyen, Maciej Rybinski. 274-285 [doi]
• End-to-end Biomedical Question Answering via Bio-AnswerFinder and Discriminative Language Representation ModelsIbrahim Burak Özyurt. 286-301 [doi]
• A Neural Text Ranking Approach for Automatic MeSH IndexingAlastair R. Rae, James G. Mork, Dina Demner-Fushman. 302-312 [doi]
• COLE and LYS at BioASQ MESINESP Task: large scale multilabel text categorization with sparse and dense indicesFrancisco J. Ribadas-Pena, Shuyuan Cao, Elmurod Kuriyozov. 313-323 [doi]
• LASIGE-BioTM at MESINESP2: entity linking with semantic similarity and extreme multi-label classification on Spanish biomedical documentsPedro Ruas, Vitor D. T. Andrade, Francisco M. Couto. 324-334 [doi]
• NLM at BioASQ Synergy 2021: Deep Learning-based Methods for Biomedical Semantic Question Answering about COVID-19Mourad Sarrouti, Deepak Gupta, Asma Ben Abacha, Dina Demner-Fushman. 335-350 [doi]
• KU-DMIS at BioASQ 9: Data-centric and model-centric approaches for biomedical question answeringWonjin Yoon, Jaehyo Yoo, Sumin Seo, Mujeen Sung, Minbyul Jeong, Gangwoo Kim, Jaewoo Kang. 351-359 [doi]
• NCU-IISR/AS-GIS: Results of Various Pre-trained Biomedical Language Models and Linear Regression Model in BioASQ Task 9b Phase BYu Zhang, Jen-Chieh Han, Richard Tzong-Han Tsai. 360-368 [doi]
• Overview of the CLEF-2021 CheckThat! Lab Task 1 on Check-Worthiness Estimation in Tweets and Political DebatesShaden Shaar, Maram Hasanain, Bayan Hamdan, Zien Sheikh Ali, Fatima Haouari, Alex Nikolov, Mücahid Kutlu, Yavuz Selim Kartal, Firoj Alam, Giovanni Da San Martino, Alberto Barrón-Cedeño, Rubén Míguez, Javier Beltrán, Tamer Elsayed, Preslav Nakov. 369-392 [doi]
• Overview of the CLEF-2021 CheckThat! Lab Task 2 on Detecting Previously Fact-Checked Claims in Tweets and Political DebatesShaden Shaar, Fatima Haouari, Watheq Mansour, Maram Hasanain, Nikolay Babulkov, Firoj Alam, Giovanni Da San Martino, Tamer Elsayed, Preslav Nakov. 393-405 [doi]
• Overview of the CLEF-2021 CheckThat! Lab: Task 3 on Fake News DetectionGautam Kishore Shahi, Julia Maria Struß, Thomas Mandl 0001. 406-423 [doi]
• QMUL-SDS at CheckThat! 2021: Enriching Pre-Trained Language Models for the Estimation of Check-Worthiness of Arabic TweetsAmani S. Abumansour, Arkaitz Zubiaga. 424-429 [doi]
• SCUoL at CheckThat! 2021: An AraBERT Model for Check-Worthiness of Arabic TweetsSaud Althabiti, Mohammad Ammar Alsalka, Eric Atwell. 430-434 [doi]
• M82B at CheckThat! 2021: Multiclass Fake News Detection Using BiLSTMSohel Siddique Ashik, Abdur Rahman Apu, Nusrat Jahan Marjana, Md. Sanzidul Islam, Md. Arid Hassan. 435-445 [doi]
• CIC at CheckThat! 2021: Fake News detection Using Machine Learning And Data AugmentationNoman Ashraf, Sabur Butt, Grigori Sidorov, Alexander F. Gelbukh. 446-454 [doi]
• MUCIC at CheckThat! 2021: FaDo-Fake News Detection and Domain Identification using Transformers EnsemblingFazlourrahman Balouchzahi, Hosahalli Lakshmaiah Shashirekha, Grigori Sidorov. 455-464 [doi]
• UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy ClaimsIpek Baris Schlicht, Angel Felipe Magnossão de Paula, Paolo Rosso. 465-475 [doi]
• SU-NLP at CheckThat! 2021: Check-Worthiness of Turkish TweetsBuse Carik, Reyyan Yeniterzi. 476-483 [doi]
• Aschern at CLEF CheckThat! 2021: Lambda-Calculus of Fact-Checked ClaimsAnton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov. 484-493 [doi]
• UAICS at CheckThat! 2021: Fake news detectionCiprian-Gabriel Cusmuliuc, Matei Alexandru Amarandei, Ioana Pelin, Vlad-Iulian Cociorva, Adrian Iftene. 494-507 [doi]
• University of Regensburg at CheckThat! 2021: Exploring Text Summarization for Fake News DetectionPhilipp Hartl, Udo Kruschwitz. 508-519 [doi]
• CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic ClassificationÁlvaro Huertas-García, Javier Huertas-Tato, Alejandro Martín, David Camacho. 520-530 [doi]
• Classifier for fake news detection and Topical Domain of News ArticlesWilliam Kana Tsoplefack. 531-536 [doi]
• Nkovachevich at CheckThat! 2021: BERT fine-tuning approach to fake news detectionNinko Kovachevich. 537-544 [doi]
• NLP&IR@UNED at CheckThat! 2021: Check-worthiness estimation and fake news detection using transformer modelsJuan R. Martinez-Rico, Juan Martínez-Romo, Lourdes Araujo. 545-557 [doi]
• DIPS at CheckThat! 2021: Verified Claim RetrievalSimona Mihaylova, Iva Borisova, Dzhovani Chemishanov, Preslav Hadzhitsanev, Momchil Hardalov, Preslav Nakov. 558-571 [doi]
• NLytics at CheckThat! 2021: Multi-class fake news detection of news articles and domain identification with RoBERTa - a baseline modelAlbert Pritzkau. 572-581 [doi]
• NLytics at CheckThat! 2021: Detecting Previously Fact-Checked Claims by Measuring Semantic SimilarityAlbert Pritzkau. 582-591 [doi]
• NLytics at CheckThat! 2021: Check-Worthiness Estimation as a Regression Problem on TransformersAlbert Pritzkau. 592-602 [doi]
• NITK_NLP at CheckThat! 2021: Ensemble Transformer Model for Fake News ClassificationHariharan RamakrishnaIyer LekshmiAmmal, Anand Kumar Madasamy. 603-611 [doi]
• Team Sigmoid at CheckThat!2021 Task 3a: Multiclass fake news detection with Machine LearningAbdullah Al Mamun Sardar, Shahalu Akter Salma, Md. Sanzidul Islam, Md. Arid Hasan, Touhid Bhuiyan. 612-618 [doi]
• Qword at CheckThat! 2021: An Extreme Gradient Boosting Approach for Multiclass Fake News DetectionRudra Sarker Utsha, Mumenunnessa Keya, Md. Arid Hasan, Md. Sanzidul Islam. 619-627 [doi]
• GPLSI team at CheckThat! 2021: Fine-tuning BETO and RoBERTaRobiert Sepúlveda-Torres, Estela Saquete. 628-638 [doi]
• BeaSku at CheckThat! 2021: Fine-Tuning Sentence BERT with Triplet Loss and Limited DataBeata Skuczynska, Shaden Shaar, Jennifer Spenader, Preslav Nakov. 639-647 [doi]
• BlackOps at CheckThat! 2021: User Profiles Analyze of Intelligent Detection on Fake Tweets Notebook for PANS. M. Sohan, Sharun Akter Khushbu, Md. Sanzidul Islam, Md. Arid Hasan. 648-658 [doi]
• Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentationEvan M. Williams, Paul Rodrigues 0001, Sieu Tran. 659-669 [doi]
• TOBB ETU at CheckThat! 2021: Data Engineering for Detecting Check-Worthy ClaimsMuhammed Said Zengin, Yavuz Selim Kartal, Mücahid Kutlu. 670-680 [doi]
• Fight for 4230 at CheckThat! 2021: Domain-Specific Preprocessing and Pretrained Model for Ranking Claims by Check-WorthinessXinrui Zhou, Bohuai Wu, Pascale Fung. 681-692 [doi]
• Extended Overview of ChEMU 2021: Reaction Reference Resolution and Anaphora Resolution in Chemical PatentsYuan Li, Biaoyan Fang, Jiayuan He, Hiyori Yoshikawa, Saber A. Akhondi, Christian Druckenbrodt, Camilo Thorne, Zubair Afzal, Zenan Zhai, Timothy Baldwin, Karin M. Verspoor. 693-709 [doi]
• A pipelined approach to Anaphora Resolution in Chemical PatentsRitam Dutt, Sopan Khosla, Carolyn P. Rosé. 710-719 [doi]
• HUKB at ChEMU 2021 Task 2: Anaphora ResolutionKojiro Machi, Masaharu Yoshioka. 720-731 [doi]
• Overview of CLEF eHealth Task 1 - SpRadIE: A challenge on information extraction from Spanish Radiology ReportsViviana Cotik, Laura Alonso Alemany, Darío Filippo, Franco Luque, Roland Roller, Jorge Vivaldi, Ammer Ayach, Fernando Carranza, Lucas Defrancesca, Antonella Dellanzo, Macarena Fernández Urquiza. 732-750 [doi]
• Consumer Health Search at CLEF eHealth 2021Lorraine 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. 751-769 [doi]
• IMS-UNIPD @ CLEF eHealth Task 1: A Memory Based Reproducible BaselineGiorgio Maria Di Nunzio. 770-774 [doi]
• IMS-UNIPD @ CLEF eHealth Task 2: Reciprocal Ranking Fusion in CHSGiorgio Maria Di Nunzio, Federica Vezzani. 775-779 [doi]
• LSI_UNED at CLEF eHealth2021: Exploring the effects of transfer learning in negation detection and entity recognition in clinical textsHermenegildo Fabregat, Andrés Duque, Lourdes Araujo, Juan Martínez-Romo. 780-793 [doi]
• Pre-trained language models to extract information from radiological reportsPilar López-Úbeda, Manuel Carlos Díaz-Galiano, Luis Alfonso Ureña López, María-Teresa Martín Valdivia. 794-803 [doi]
• Extracting information from radiology reports by Natural Language Processing and Deep LearningMiguel Ángel Martín-Caro García-Largo, Isabel Segura-Bedmar. 804-817 [doi]
• Comparing Transformer-based NER approaches for analysing textual medical diagnosesMarco Polignano, Marco de Gemmis, Giovanni Semeraro. 818-833 [doi]
• Information Extraction from Spanish Radiology Reports using multilingual BERTOswaldo Solarte Pabón, Orlando Montenegro, Alberto Blázquez-Herranz, Hadi Saputro, Alejandro Rodríguez González, Ernestina Menasalvas. 834-845 [doi]
• A multi-BERT hybrid system for Named Entity Recognition in Spanish radiology reportsVíctor Suárez-Paniagua, Hang Dong, Arlene Casey. 846-856 [doi]
• Learning to rank for Consumer Health SearchHua Yang, Xiaoming Liu, Binbin Zheng, Guan Yang. 857-863 [doi]
• Overview of eRisk at CLEF 2021: Early Risk Prediction on the Internet (Extended Overview)Javier Parapar, Patricia Martín-Rodilla, David E. Losada, Fabio Crestani. 864-887 [doi]
• Predicting Sign of Depression via Using Frozen Pre-trained Models and Random Forest ClassifierHassan Alhuzali, Tianlin Zhang, Sophia Ananiadou. 888-896 [doi]
• VADER meets BERT: sentiment analysis for early detection of signs of self-harm through social miningLucas Barros, Alina Trifan, José Luís Oliveira. 897-907 [doi]
• UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the InternetAngelo Basile, Mara Chinea-Rios, Ana Sabina Uban, Thomas Müller, Luise Rössler, Seren Yenikent, Berta Chulvi, Paolo Rosso, Marc Franco-Salvador. 908-927 [doi]
• Exploring the Performance of Baseline Text Mining Frameworks for Early Prediction of Self Harm Over Social MediaTanmay Basu, Georgios V. Gkoutos. 928-937 [doi]
• Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTAna-Maria Bucur, Adrian Cosma, Liviu P. Dinu. 938-949 [doi]
• NLP-UNED at eRisk 2021: self-harm early risk detection with TF-IDF and linguistic featuresElena Campillo Ageitos, Hermenegildo Fabregat, Lourdes Araujo, Juan Martínez-Romo. 950-965 [doi]
• uOttawa at eRisk 2021: Automatic Filling of the Beck's Depression Inventory Questionnaire using Deep LearningDiana Inkpen, Ruba Skaik, Prasadith Buddhitha, Dimo Angelov, Maxwell Thomas Fredenburgh. 966-980 [doi]
• CeDRI at eRisk 2021: A Naive Approach to Early Detection of Psychological Disorders in Social MediaRui Pedro Lopes. 981-991 [doi]
• UNSL at eRisk 2021: A Comparison of Three Early Alert Policies for Early Risk DetectionJuan Martín Loyola, Sergio Burdisso, Horacio Thompson, Leticia C. Cagnina, Marcelo Errecalde. 992-1021 [doi]
• UniOR NLP at eRisk 2021: Assessing the Severity of Depression with Part of Speech and Syntactic FeaturesRaffaele Manna, Johanna Monti. 1022-1030 [doi]
• Early Detection of Signs of Pathological Gambling, Self-Harm and Depression through Topic Extraction and Neural NetworksDiego Maupomé, Maxime D. Armstrong, Fanny Rancourt, Thomas Soulas, Marie-Jean Meurs. 1031-1045 [doi]
• Transfer Learning for Automated Responses to the BDI QuestionnaireChristoforos Spartalis, George Drosatos, Avi Arampatzis. 1046-1058 [doi]
• Towards transfer learning using BERT for early detection of self-harm of social media usersQamar Un Nisa, Rafi Muhammad. 1059-1070 [doi]
• A RoBERTa-based model on measuring the severity of the signs of depressionShih-Hung Wu, Zhao-Jun Qiu. 1071-1080 [doi]
• Overview of the VQA-Med Task at ImageCLEF 2021: Visual Question Answering and Generation in the Medical DomainAsma Ben Abacha, Mourad Sarrouti, Dina Demner-Fushman, Sadid A. Hasan, Henning Müller. 1081-1088 [doi]
• Overview of ImageCLEFtuberculosis 2021 - CT-based Tuberculosis Type ClassificationSerge Kozlovski, Vitali Liauchuk, Yashin Dicente Cid, Vassili Kovalev, Henning Müller. 1089-1100 [doi]
• Overview of the ImageCLEFmed 2021 Concept & Caption Prediction TaskObioma Pelka, Asma Ben Abacha, Alba Garcia Seco de Herrera, Janadhip Jacutprakart, Christoph M. Friedrich, Henning Müller. 1101-1112 [doi]
• Overview of ImageCLEFcoral 2021: Coral Reef Image Annotation of a 3D EnvironmentJon Chamberlain, Alba García Seco de Herrera, Antonio Campello, Adrian Clark, Thomas Oliver, Hassan Moustahfid. 1113-1120 [doi]
• Overview of the 2021 ImageCLEFdrawnUI Task: Detection and Recognition of Hand Drawn and Digital Website UIsRaul Berari, Andrei Tauteanu, Dimitri Fichou, Paul Brie, Mihai Dogariu, Liviu-Daniel Stefan, Mihai Gabriel Constantin, Bogdan Ionescu. 1121-1132 [doi]
• Classification of Tuberculosis Type on CT Scans of Lungs using a fusion of 2D and 3D Deep Convolutional Neural NetworksEmad Aghajanzadeh, Behzad Shomali, Diba Aminshahidi, Navid Ghassemi. 1133-1144 [doi]
• Simple Neural Network based TB ClassificationAnirudh Anand, Karthik Raja Anandan, Bhuvana Jayaraman, Mirnalinee Thanga Nadar Thanga Thai. 1145-1150 [doi]
• ImageCLEF 2021: Deep categorizing tuberculosis cases using normalization and pseudo-color CT imageTetsuya Asakawa, Riku Tsuneda, Kazuki Shimizu, Takuyuki Komoda, Masaki Aono. 1151-1159 [doi]
• Attention-based CNN-GRU Model For Automatic Medical Images Captioning: ImageCLEF 2021Djamila Romaissa Beddiar, Mourad Oussalah 0001, Tapio Seppänen. 1160-1173 [doi]
• PUC Chile team at Caption Prediction: ResNet visual encoding and caption classification with Parametric ReLUVicente Castro, Pablo Pino, Denis Parra, Hans Lobel. 1174-1183 [doi]
• AUEB NLP Group at ImageCLEFmed Caption Tasks 2021Foivos Charalampakos, Vasilis Karatzas, Vasiliki Kougia, John Pavlopoulos, Ion Androutsopoulos. 1184-1200 [doi]
• Chabbiimen at VQA-Med 2021: Visual Generation of Relevant Natural Language Questions from Radiology Images for Anomaly DetectionImen Chebbi. 1201-1210 [doi]
• TeamS at VQA-Med 2021: BBN-Orchestra for Long-tailed Medical Visual Question AnsweringSedigheh Eslami, Gerard de Melo, Christoph Meinel. 1211-1217 [doi]
• SYSU-HCP at VQA-Med 2021: A Data-centric Model with Efficient Training Methodology for Medical Visual Question AnsweringHaifan Gong, Ricong Huang, Guanqi Chen, Guanbin Li. 1218-1228 [doi]
• A Convolutional Neural Networks based Coral Reef Annotation and LocalizationRohit Gunti, Abebe Rorissa. 1229-1238 [doi]
• UI element detection from wireframe drawings of websitesPrasang Gupta, Vishakha Bansal. 1239-1252 [doi]
• UAIC2021: Lung Analysis for Tuberculosis ClassificationAlexandra Hanganu, Cristian Simionescu, Lucia Georgiana Coca, Adrian Iftene. 1253-1263 [doi]
• NLIP-Essex-ITESM at ImageCLEFcaption 2021 task : Deep Learning-based Information Retrieval and Multi-label Classification towards improving Medical Image UnderstandingJanadhip Jacutprakart, Francisco Parrilla Andrade, Rodolfo Cuan, Arely Aceves Compean, Giorgos Papanastasiou, Alba Garcia Seco de Herrera. 1264-1274 [doi]
• Lijie at ImageCLEFmed VQA-Med 2021: Attention Model-based Efficient Interaction between MultimodalityJie Li, Shengyan Liu. 1275-1284 [doi]
• Lijie at ImageCLEFmed Tuberculosis 2021: EfficientNet Simplified Tuberculosis Case ClassificationJie Li, Li Yang, Yang Bai. 1285-1294 [doi]
• TAM at VQA-Med 2021: A Hybrid Model with Feature Extraction and Fusion for Medical Visual Question AnsweringYong Li, Zhenguo Yang, Tianyong Hao. 1295-1304 [doi]
• Multi-Classification Study of the Tuberculosis with 3D CBAM-ResNet and EfficientNetXing Lu, Eric Y. Chang, Chun-Nan Hsu, Jiang Du, Amilcare Gentili. 1305-1309 [doi]
• Identifying tuberculosis type in CTsCosmin Moisii, Radu Miron 0002, Mihaela Breaban. 1310-1316 [doi]
• AEHRC CSIRO at ImageCLEFmed Caption 2021Aaron Nicolson, Jason Dowling, Bevan Koopman. 1317-1328 [doi]
• SSN MLRG at VQA-MED 2021: An Approach for VQA to Solve Abnormality Related Queries using Improved DatasetsNoor Mohamed Sheerin Sitara, Kavitha Srinivasan. 1329-1335 [doi]
• PUC Chile team at TBT Task: Diagnosis of Tuberculosis Type using segmented CT scansJosé Miguel Quintana, Daniel Florea, Ria Deane, Denis Parra, Pablo Pino, Pablo Messina, Hans Löbel. 1336-1345 [doi]
• PUC Chile team at VQA-Med 2021: approaching VQA as a classification task via fine-tuning a pretrained CNNRicardo Schilling, Pablo Messina, Denis Parra, Hans Löbel. 1346-1351 [doi]
• PUC Chile team at Concept Detection: K Nearest Neighbors with Perceptual SimilarityGregory Schuit, Vicente Castro, Pablo Pino, Denis Parra, Hans Lobel. 1352-1358 [doi]
• Automatic Coral Reef Annotation, Localization and Pixel-wise Parsing Using Mask R-CNNLukás Soukup. 1359-1364 [doi]
• Kdelab at ImageCLEF 2021: Medical Caption Prediction with Effective Data Pre-processing and Deep LearningRiku Tsuneda, Tetsuya Asakawa, Masaki Aono. 1365-1374 [doi]
• Improving web user interface element detection using Faster R-CNNJirí Vyskocil, Lukás Picek. 1375-1386 [doi]
• ImageSem Group at ImageCLEFmed Caption 2021 Task: Exploring the Clinical Significance of the Textual Descriptions Derived from Medical ImagesXuwen Wang, Zhen Guo, Chunyuan Xu, Lianglong Sun, Jiao Li. 1387-1393 [doi]
• Pixelwise annotation of coral reef substratesJessica P. Wright, Ioana-Lia Palosanu, Louis G. Clift, Alba García Seco de Herrera, Jon Chamberlain. 1394-1404 [doi]
• Yunnan University at VQA-Med 2021: Pretrained BioBERT for Medical Domain Visual Question AnsweringQian Xiao, Xiaobing Zhou, Ya Xiao, Kun Zhao. 1405-1411 [doi]
• ViPTT-Net: Video pretraining of spatio-temporal model for tuberculosis type classification from chest CT scansHasib Zunair, Aimon Rahman, Nabeel Mohammed. 1412-1421 [doi]
• Overview of PlantCLEF 2021: cross-domain plant identificationHervé Goëau, Pierre Bonnet, Alexis Joly. 1422-1436 [doi]
• Overview of BirdCLEF 2021: Bird call identification in soundscape recordingsStefan Kahl, Tom Denton, Holger Klinck, Hervé Glotin, Hervé Goëau, Willem-Pier Vellinga, Robert Planqué, Alexis Joly. 1437-1450 [doi]
• Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing imagesTitouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, Alexis Joly. 1451-1462 [doi]
• Overview of SnakeCLEF 2021: Automatic Snake Species Identification with Country-Level FocusLukás Picek, Andrew Durso, Isabelle Bolon, Rafael Luis Ruiz De Castaneda. 1463-1476 [doi]
• EfficientNets and Vision Transformers for Snake Species Identification Using Image and Location InformationLouise Bloch, Christoph M. Friedrich. 1477-1498 [doi]
• Incorporation of object detection models and location data into snake species classificationRego Borsodi, Dávid Papp. 1499-1511 [doi]
• A Deep Learning Method for Visual Recognition of Snake SpeciesRail Chamidullin, Milan Sulc, Jiri Matas, Lukás Picek. 1512-1525 [doi]
• Improved Herbarium-Field Triplet Network for Cross-Domain Plant Identification: NEUON Submission to LifeCLEF 2021 PlantSophia Chulif, Yang Loong Chang. 1526-1539 [doi]
• UAIC-AI at SnakeCLEF 2021: Impact of convolutions in snake species recognitionLucia Georgiana Coca, Alexia Theodora Popa, Razvan Contantin Croitoru, Luciana Paraschiva Bejan, Adrian Iftene. 1540-1546 [doi]
• Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 SolutionMarcos V. Conde, Kumar Shubham, Prateek Agnihotri, Nitin D. Movva, Szilard Bessenyei. 1547-1558 [doi]
• Bird-Species Audio Identification, Ensembling 1D + 2D SignalsGyanendra Das, Saksham Aggarwal. 1559-1570 [doi]
• Snake Species Classification using Transfer Learning TechniqueKarthik Desingu, Mirunalini Palaniappan, Jitesh Kumar. 1571-1578 [doi]
• Recognizing bird species in diverse soundscapes under weak supervisionChristof Henkel, Pascal Pfeiffer, Philipp Singer. 1579-1586 [doi]
• Automatic Snake Classification using Deep Learning AlgorithmLekshmi Kalinathan, Prabavathy Balasundaram, Pradeep Ganesh, Sandeep Sekhar Bathala, Rahul Kumar Mukesh. 1587-1596 [doi]
• Birdcall Identification Using CNN and Gradient Boosting Decision Trees with Weak and Noisy SupervisionNaoki Murakami, Hajime Tanaka, Masataka Nishimori. 1597-1608 [doi]
• STFT Transformers for Bird Song RecognitionJean-Francois Puget. 1609-1616 [doi]
• TUC Media Computing at BIRDCLEF 2021: Noise augmentation strategies in bird sound classification in combination with DenseNets and ResNetsArunodhayan Sampath Kumar, Danny Kowerko. 1617-1626 [doi]
• Learning to Monitor Birdcalls From Weakly-Labeled Focused RecordingsJan Schlüter. 1627-1638 [doi]
• Contrastive Representation Learning for Natural World Imagery: Habitat prediction for 30, 000 speciesSachith Seneviratne. 1639-1648 [doi]
• BirdCLEF 2021: building a birdcall segmentation model based on weak labelsMaxim Shugaev, Naoya Tanahashi, Philip Dhingra, Urvish Patel. 1649-1658 [doi]
• Weighted Pseudo Labeling Refinement for Plant IdentificationYoushan Zhang, Brian D. Davison 0001. 1659-1667 [doi]
• Overview of LiLAS 2021 - Living Labs for Academic Search (Extended Overview)Philipp Schaer, Timo Breuer, Leyla Jael Castro, Benjamin Wolff, Johann Schaible, Narges Tavakolpoursaleh. 1668-1699 [doi]
• TEKMA at CLEF-2021: BM-25 based rankings for scientific publication retrieval and data set recommendationJüri Keller, Leon Paul Mondrian Munz. 1700-1711 [doi]
• PyTerrier-based Research Data Recommendations for Scientific Articles in the Social SciencesNarges Tavakolpoursaleh, Johann Schaible. 1712-1722 [doi]
• Ad-hoc Retrieval of scientific Documents on the LIVIVO Search PortalAnh Huy Matthias Tran, Andreas Kruff, Joshua Thos, Constantin Krah, Michelle Reiners, Fabian Ax, Saskia Brech, Sascha Gharib, Verena Pawlas. 1723-1742 [doi]
• Overview of the Cross-Domain Authorship Verification Task at PAN 2021Mike Kestemont, Enrique Manjavacas, Ilia Markov, Janek Bevendorff, Matti Wiegmann, Efstathios Stamatatos, Benno Stein 0001, Martin Potthast. 1743-1759 [doi]
• Overview of the Style Change Detection Task at PAN 2021Eva Zangerle, Maximilian Mayerl, Martin Potthast, Benno Stein 0001. 1760-1771 [doi]
• Profiling Hate Speech Spreaders on Twitter Task at PAN 2021Francisco Rangel, Gretel Liz De la Peña Sarracén, Berta Chulvi, Elisabetta Fersini, Paolo Rosso. 1772-1789 [doi]
• Profiling Hate Spreaders using word N-gramsJorge Alcañiz, José Andrés. 1790-1795 [doi]
• Profiling Hate Speech Spreaders by Classifying Micro Texts Using BERT ModelEsam Alzahrani, Leon Jololian. 1796-1800 [doi]
• Profiling Hate Speech Spreaders on TwitterÀngel Andújar Carracedo, Raquel Jiménez Mondéjar. 1801-1807 [doi]
• Identify Hate Speech Spreaders on Twitter using Transformer Embeddings Features and AutoML ClassifiersTalha Anwar. 1808-1812 [doi]
• Profiling Haters on Twitter using Statistical and Contextualized EmbeddingsHamed Babaei Giglou, Taher Rahgooy, Jafar Razmara, Mostafa Rahgouy, Zahra Rahgooy. 1813-1821 [doi]
• Profiling Spreaders of Hate Speech with N-grams and RoBERTaChristopher Bagdon. 1822-1828 [doi]
• HSSD: Hate Speech Spreader Detection using N-grams and Voting ClassifierFazlourrahman Balouchzahi, Shashirekha Hosahalli Lakshmaiah, Grigori Sidorov. 1829-1836 [doi]
• Unified and Multilingual Author Profiling for Detecting HatersIpek Baris Schlicht, Angel Felipe Magnossão de Paula. 1837-1845 [doi]
• O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship VerificationBenedikt T. Bönninghoff, Robert M. Nickel, Dorothea Kolossa. 1846-1857 [doi]
• INFOTEC-LaBD at PAN@CLEF21: Profiling Hate Speech Spreaders on Twitter through Emotion-based RepresentationsHiram Cabrera, Sabino Miranda-Jiménez, Eric Sadit Tellez. 1858-1870 [doi]
• Exploiting Contextualized Word Representations to Profile Haters on TwitterTanise Ceron, Camilla Casula. 1871-1882 [doi]
• Use of Lexical and Psycho-Emotional Information to Detect Hate Speech Spreaders on Twitter - Notebook for PAN at CLEF 2021Riccardo Cervero. 1883-1891 [doi]
• Profiling Hate Speech Spreaders on TwitterKumar Gourav Das, Buddhadeb Garai, Srijan Das, Braja Gopal Patra. 1892-1898 [doi]
• Style Change Detection on Real-World Data using an LSTM-powered Attribution AlgorithmRobert Deibel, Denise Löfflad. 1899-1909 [doi]
• Detection of Hate Speech Spreaders with BERTDavid Dukic, Ana Sovic Krzic. 1910-1919 [doi]
• Graph-based Siamese Network for Authorship VerificationDaniel Embarcadero-Ruiz, Helena Gómez-Adorno, Ivan Reyes-Hernández, Alexis García, Alberto Embarcadero-Ruiz. 1920-1930 [doi]
• Profiling Hate Speech Spreaders using Characters andWords N-gramsDaniel Yacob Espinosa, Grigori Sidorov. 1931-1936 [doi]
• Hate speech spreader detection using contextualized word embeddingsEvgeny Finogeev, Mariam Kaprielova, Artem Chashchin, Kirill Grashchenkov, George Gorbachev, Oleg Bakhteev. 1937-1944 [doi]
• Author classification as pre-training for pairwise authorship verificationRomain Futrzynski. 1945-1952 [doi]
• Profiling Hate Speech Spreaders on Twitter using stylistic features and word embeddingsLucía Gómez-Zaragozá, Sara Hinojosa Pinto. 1953-1962 [doi]
• Profiling Hate Speech Spreaders on Twitter: Transformers and mixed poolingÁlvaro Huertas-García, Javier Huertas-Tato, Alejandro Martín, David Camacho. 1963-1975 [doi]
• Effective Detection of Hate Speech Spreaders on TwitterJulian Höllig, Yeong Su Lee, Nina Seemann, Michaela Geierhos. 1976-1986 [doi]
• UniNE at PAN-CLEF 2021Catherine Ikae. 1987-1994 [doi]
• UniNE at PAN-CLEF 2021: Authorship VerificationCatherine Ikae. 1995-2003 [doi]
• Early Detection of Online Hate Speech Spreaders with Learned User RepresentationsDarius Irani, Avyakta Wrat, Silvio Amir. 2004-2010 [doi]
• Profiling Hate Speech Spreaders on TwitterRakshita Jain, Devanshi Goel, Prashant Sahu, Abhinav Kumar, Jyoti Prakash Singh. 2011-2024 [doi]
• Using N-grams and Statistical Features to Identify Hate Speech Spreaders on TwitterEszter Katona, Jakab Buda, Flora Bolonyai. 2025-2034 [doi]
• Deep Modeling of Latent Representations for Twitter Profiles on Hate Speech Spreaders Identification. Notebook for PAN at CLEF 2021Roberto Labadie, Daniel Castro-Castro, Reynier Ortega Bueno. 2035-2046 [doi]
• HaMor at the Profiling Hate Speech Spreaders on TwitterMirko Lai, Marco Antonio Stranisci, Cristina Bosco, Rossana Damiano, Viviana Patti. 2047-2055 [doi]
• Authorship Verification based on Lucene architectureZhihao Liao, Yong Han, Leilei Kong, Zhuopeng Hong, Zijian Li 0006, Guiyuan Liang, Zhenwei Mo, Zhixian Li, Zhongyuan Han. 2056-2059 [doi]
• Hate Speech Detection on TwitterCarolina Martín-Del-Campo-Rodríguez, Grigori Sidorov, Ildar Z. Batyrshin. 2060-2063 [doi]
• Authorship Verification with neural networks via stylometric feature concatenationAntonio Menta Garuz, Ana García-Serrano. 2064-2068 [doi]
• Dual Neural Network Classification Based on BERT Feature Extraction for Authorship VerificationXiaogang Miao, Haoliang Qi, Zhijie Zhang, Guiyuan Cao, Ruilan Lin, Wenbin Lin. 2069-2072 [doi]
• Style change detection using Siamese neural networksSukanya Nath. 2073-2082 [doi]
• University of Regensburg @ PAN: Profiling Hate Speech Spreaders on TwitterKwabena Odame Akomeah, Udo Kruschwitz, Bernd Ludwig. 2083-2089 [doi]
• Local Classification with Recurrent Neural Network for Profiling Hate Speech Spreaders on Twitter. Notebook for PAN at CLEF 2021Pablo Pallarés, Carlos Herrero. 2090-2102 [doi]
• Encoding Text Information By Pre-trained Model For Authorship VerificationZeyang Peng, Leilei Kong, Zhijie Zhang, Zhongyuan Han, Xu Sun. 2103-2107 [doi]
• Feature Similarity-based Regression Models for Authorship VerificationMarina Pinzhakova, Tom Yagel, Jakov Rabinovits. 2108-2117 [doi]
• Phonetic Detection for Hate Speech Spreaders on TwitterEdwin Puertas, Juan Carlos Martínez Santos. 2118-2125 [doi]
• Detection of hate speech spreaders using convolutional neural networksMarco Siino, Elisa Di Nuovo, Ilenia Tinnirello, Marco La Cascia. 2126-2136 [doi]
• Writing Style Change Detection on Multi-Author DocumentsRhia Singh, Janith Weerasinghe, Rachel Greenstadt. 2137-2145 [doi]
• Multi-label Style Change Detection by Solving a Binary Classification ProblemEivind Strøm. 2146-2157 [doi]
• Multi-level stacked ensemble with sparse and dense features for hate speech detection on TwitterDarko Tosev, Sonja Gievska. 2158-2168 [doi]
• Siamese Bert for Authorship VerificationJacob Tyo, Bhuwan Dhingra, Zachary Lipton. 2169-2177 [doi]
• Detecting Hate Speech Spreaders on Twitter using LSTM and BERT in English and SpanishMoshe Uzan, Yaakov HaCohen-Kerner. 2178-2185 [doi]
• Profiling Hate Speech Spreaders on Twitter: Exploiting Textual Analysis of Tweets and Combination of Textual RepresentationsCláudio Moisés Valiense de Andrade, Marcos André Gonçalves. 2186-2192 [doi]
• Profiling Hate Speech Spreaders on Twitter: SVM vs. Bi-LSTMInna Vogel, Meghana Meghana. 2193-2200 [doi]
• Feature Vector Difference based Authorship Verification for Open-World SettingsJanith Weerasinghe, Rhia Singh, Rachel Greenstadt. 2201-2207 [doi]
• Style Change Detection Based On Writing Style SimilarityZhijie Zhang, Zhongyuan Han, Leilei Kong, Xiaogang Miao, Zeyang Peng, Jieming Zeng, Haojie Cao, Jinxi Zhang, Ziwei Xiao, Xuemei Peng. 2208-2211 [doi]
• Overview of SimpleText CLEF 2021 Workshop and Pilot TasksLiana Ermakova, Patrice Bellot, Pavel Braslavski, Jaap Kamps, Josiane Mothe, Diana Nurbakova, Irina Ovchinnikova, Eric SanJuan. 2212-2227 [doi]
• Overview of the SimpleText Workshop at INFORSID-2021: Scientific Text Simplification and PopularizationLiana Ermakova, Josiane Mothe, Eric SanJuan. 2228-2232 [doi]
• Multimodal science communication: from documentary research to infographic via mind mappingSílvia Araújo, Radia Hannachi. 2233-2236 [doi]
• LIA@SimpleText2021: automatic query extraction from press outlets for mining related readable passages in scientific literatureMalek Hajjem, Eric SanJuan. 2237-2241 [doi]
• What Science-Related Topics Need to Be Popularized? A Comparative StudyIrina Ovchinnikova, Diana Nurbakova, Liana Ermakova. 2242-2255 [doi]
• Importance of Data and Controllability in Neural Text SimplificationWei Xu. 2256-2257 [doi]
• Overview of Touché 2021: Argument RetrievalAlexander Bondarenko, Lukas Gienapp, Maik Fröbe, Meriem Beloucif, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein 0001, Henning Wachsmuth, Martin Potthast, Matthias Hagen. 2258-2284 [doi]
• Exploring Argument Retrieval for Controversial Questions Using Retrieve and Re-rank PipelinesRaunak Agarwal, Andrei Koniaev, Robin Schaefer. 2285-2291 [doi]
• Learning to Rank Arguments with Feature SelectionChristopher Akiki, Maik Fröbe, Matthias Hagen, Martin Potthast. 2292-2301 [doi]
• Development of an IR System for Argument SearchMarco Alecci, Tommaso Baldo, Luca Martinelli, Elia Ziroldo. 2302-2318 [doi]
• DistilBERT-based Argumentation Retrieval for Answering Comparative QuestionsAlaa Alhamzeh, Mohamed Bouhaouel, Elöd Egyed-Zsigmond, Jelena Mitrovic. 2319-2330 [doi]
• Document retrieval task on controversial topic with Re-Ranking approachAndrea Cassetta, Alberto Piva, Enrico Vicentini. 2331-2353 [doi]
• Retrieving Comparative Arguments using Ensemble Methods and BERTViktoria Chekalina, Alexander Panchenko. 2354-2365 [doi]
• Quality-aware Argument Retrieval with Topical ClusteringLukas Gienapp. 2366-2373 [doi]
• Exploring BERT Synonyms and Quality Prediction for Argument RetrievalTommaso Green, Luca Moroldo, Alberto Valente. 2374-2388 [doi]
• Touché Task 2: Comparative Argument Retrieval A document-based search engine for answering comparative questionsDaniel Helmrich, Denis Streitmatter, Fionn Fuchs, Maximilian Heykeroth. 2389-2402 [doi]
• Argument Retrieval for Comparative Questions based on independent featuresThi Kim Hanh Luu, Jan-Niklas Weder. 2403-2416 [doi]
• Exploring Document Expansion for Argument RetrievalAlina Mailach, Denise Arnold, Stefan Eysoldt, Simon Kleine. 2417-2422 [doi]
• A Search Engine System for Touché Argument Retrieval task to answer Controversial QuestionsEdoardo Raimondi, Marco Alessio, Nicola Levorato. 2423-2440 [doi]
• Team Skeletor at Touché 2021: Argument Retrieval and Visualization for Controversial QuestionsKevin Ros, Carl Edwards, Heng Ji, ChengXiang Zhai. 2441-2454 [doi]
• Thor at Touché 2021: Argument Retrieval for Comparative QuestionsEkaterina Shirshakova, Ahmad Wattar. 2455-2462 [doi]