Table of Contents

• Overview of MedProcNER Task on Medical Procedure Detection and Entity Linking at BioASQ 2023Salvador Lima-López, Eulàlia Farré-Maduell, Luis Gascó, Anastasios Nentidis, Anastasia Krithara, Georgios Katsimpras, Georgios Paliouras, Martin Krallinger. 1-18 [doi]
• Overview of BioASQ Tasks 11b and Synergy11 in CLEF2023Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, Georgios Paliouras. 19-26 [doi]
• Improving Biomedical Question Answering with Sentence-based Ranking at BioASQ-11bAnna Aksenova, Tsvetan Asamov, Petar Ivanov, Svetla Boytcheva. 27-36 [doi]
• BIT.UA at BioASQ 11B: Two-Stage IR with Synthetic Training and Zero-Shot Answer GenerationTiago Almeida, Richard Adolph Aires Jonker, Roshan Poudel, Jorge M. Silva, Sérgio Matos. 37-59 [doi]
• BIT.UA at MedProcNer: Discovering Medical Procedures in Spanish Using Transformer Models with MCRF and AugmentationTiago Almeida, Richard A. A. Jonker, Roshan Poudel, Jorge M. Silva, Sérgio Matos. 60-72 [doi]
• Is ChatGPT a Biomedical Expert?Samy Ateia, Udo Kruschwitz. 73-90 [doi]
• Coming a Long Way with Pre-Trained Transformers and String Matching Techniques: Clinical Procedure Mention Recognition and NormalizationMariia Chizhikova, Jaime Collado-Montañez, Manuel Carlos Díaz-Galiano, Luis Alfonso Ureña López, María-Teresa Martín Valdivia. 91-101 [doi]
• Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-TrainingDima Galat, Marian-Andrei Rizoiu. 102-113 [doi]
• NCU-IISR: Prompt Engineering on GPT-4 to Stove Biological Problems in BioASQ 11b Phase BChun-Yu Hsueh, Yu Zhang, Yu-Wei Lu, Jen-Chieh Han, Wilailack Meesawad, Richard Tzong-Han Tsai. 114-121 [doi]
• Leveraging Biomedical Ontologies for Clinical Procedures Recognition in Spanish at BioASQ MedProcNERPetar Ivanov, Anna Aksenova, Tsvetan Asamov, Svetla Boytcheva. 122-131 [doi]
• Exploring Approaches to Answer Biomedical Questions: From Pre-processing to GPT-4Hyunjae Kim, Hyeon Hwang, Chaeeun Lee, Minju Seo, Wonjin Yoon, Jaewoo Kang. 132-144 [doi]
• BioASQ 11B: Integrating Domain Specific Vocabulary to BERT-based Model for Biomedical Document RankingMaël Lesavourey, Gilles Hubert. 145-151 [doi]
• Semi-supervised training for biomedical question answeringDimitra N. Panou, Martin Reczko. 152-158 [doi]
• Biomedical Question Answering with Transformer EnsemblesRaghav R, Jason Rauchwerk, Parth Rajwade, Tanay Gummadi, Eric Nyberg, Teruko Mitamura. 159-167 [doi]
• Deep Metric Learning for Effective Passage Retrieval in the BioASQ ChallengeAndrés Rosso-Mateus, León A. Muñoz-Serna, Manuel Montes-y-Gómez, Fabio A. González 0001. 168-177 [doi]
• Multi-stage Literature Retrieval System Trained by PubMed Search Logs for Biomedical Question AnsweringAshley Shin, Qiao Jin 0001, Zhiyong Lu. 178-189 [doi]
• Fusion @ BioASQ MedProcNER: Transformer-based Approach for Procedure Recognition and Linking in Spanish Clinical TextSylvia Vassileva, Georgi Grazhdanski, Svetla Boytcheva, Ivan Koychev. 190-205 [doi]
• VICOMTECH at MedProcNER 2023: Transformers-based Sequence-labelling and Cross-encoding for Entity Detection and Normalisation in Spanish Clinical TextsElena Zotova, Aitor García Pablos, Montse Cuadros, German Rigau. 206-218 [doi]
• Overview of the CLEF-2023 CheckThat! Lab Task 1 on Check-Worthiness in Multimodal and Multigenre ContentFiroj Alam, Alberto Barrón-Cedeño, Gullal S. Cheema, Gautam Kishore Shahi, Sherzod Hakimov, Maram Hasanain, Chengkai Li, Rubén Míguez, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov. 219-235 [doi]
• Overview of the CLEF-2023 CheckThat! Lab: Task 2 on Subjectivity DetectionAndrea Galassi, Federico Ruggeri, Alberto Barrón-Cedeño, Firoj Alam, Tommaso Caselli, Mücahid Kutlu, Julia Maria Struß, Francesco Antici, Maram Hasanain, Juliane Köhler, Katerina Korre, Folkert Leistra, Arianna Muti, Melanie Siegel, Mehmet Deniz Türkmen, Michael Wiegand, Wajdi Zaghouani. 236-249 [doi]
• Overview of the CLEF-2023 CheckThat! Lab Task 3 on Political Bias of News Articles and News MediaGiovanni Da San Martino, Firoj Alam, Maram Hasanain, Rabindra Nath Nandi, Dilshod Azizov, Preslav Nakov. 250-259 [doi]
• Overview of the CLEF-2023 CheckThat! Lab Task 4 on Factuality of Reporting of News MediaPreslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayot Panayotov. 260-268 [doi]
• Overview of the CLEF-2023 CheckThat! Lab Task 5 on Authority Finding in TwitterFatima Haouari, Tamer Elsayed, Zien Sheikh Ali. 269-278 [doi]
• CSECU-DSG at CheckThat!-2023: Transformer-based Fusion Approach for Multimodal and Multigenre Check-WorthinessAbdul Aziz, Md. Akram Hossain, Abu Nowshed Chy. 279-288 [doi]
• Frank at CheckThat!-2023: Detecting the Political Bias of News Articles and News MediaDilshod Azizov, Preslav Nakov, Shangsong Liang. 289-305 [doi]
• DWReCO at CheckThat!-2023: Enhancing Subjectivity Detection through Style-based Data SamplingIpek Baris Schlicht, Lynn Khellaf, Defne Altiok. 306-317 [doi]
• NN at CheckThat!-2023: Subjectivity in News Articles Classification with Transformer Based ModelsKrishno Dey, Prerona Tarannum, Md. Arid Hasan, Sheak Rashed Haider Noori. 318-328 [doi]
• Fraunhofer SIT at CheckThat!-2023: Can LLMs Be Used for Data Augmentation & Few-Shot Classification? Detecting Subjectivity in Text Using ChatGPTRaphael Antonius Frick. 329-336 [doi]
• Fraunhofer SIT at CheckThat!-2023: Enhancing the Detection of Multimodal and Multigenre Check-Worthiness Using Optical Character Recognition and Model SoupingRaphael Antonius Frick, Inna Vogel, Jeong-Eun Choi. 337-350 [doi]
• Thesis Titan at CheckThat!-2023: Language-Specific Fine-tuning of mDeBERTaV3 for Subjectivity DetectionFolkert Atze Leistra, Tommaso Caselli. 351-359 [doi]
• CUCPLUS at CheckThat!-2023: Text Combination and Regularized Adversarial Training for News Media Factuality EvaluationChenxin Li, Ruijin Xue, Chichen Lin, Weijian Fan, Xiao Han. 360-371 [doi]
• NLPIR-UNED at CheckThat!-2023: Ensemble of Classifiers for Check-Worthiness EstimationJuan R. Martinez-Rico, Lourdes Araujo, Juan Martínez-Romo. 372-382 [doi]
• DSHacker at CheckThat!-2023: Check-Worthiness in Multigenre and Multilingual Content With GPT-3.5 Data AugmentationArkadiusz Modzelewski, Witold Sosnowski, Adam Wierzbicki. 383-393 [doi]
• TeamX at CheckThat!-2023: Multilingual and Multimodal Approach for Check-Worthines DetectionRabindra Nath Nandi, Firoj Alam. 394-403 [doi]
• Gpachov at CheckThat!-2023: A Diverse Multi-approach Ensemble for Subjectivity Detection in News ArticlesGeorgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov. 404-412 [doi]
• ES-VRAI at CheckThat!-2023: Leveraging Bio and Lists Information for Enhanced Rumor Verification in TwitterHamza Tarik Sadouk, Faouzi Sebbak, Hussem Eddine Zekiri. 413-422 [doi]
• ES-VRAI at CheckThat!-2023: Enhancing Model Performance for Subjectivity Detection through Multilingual Data AggregationHamza Tarik Sadouk, Faouzi Sebbak, Hussem Eddine Zekiri. 423-429 [doi]
• ES-VRAI at CheckThat!-2023: Analyzing Checkworthiness in Multimodal and Multigenre Contents through Fusion and Sampling ApproachesHamza Tarik Sadouk, Faouzi Sebbak, Hussem Eddine Zekiri. 430-444 [doi]
• FakeDTML at CheckThat!-2023: Identifying Check-Worthiness of Tweets and Debate SnippetsAbdullah Al Mamun Sardar, Md. Ziaul Karim, Krishno Dey, Md. Arid Hasan. 445-452 [doi]
• OpenFact at CheckThat!-2023: Head-to-Head GPT vs. BERT - A Comparative Study of Transformers Language Models for the Detection of Check-worthy ClaimsMarcin Sawinski, Krzysztof Wecel, Ewelina Ksiezniak, Milena Strózyna, Wlodzimierz Lewoniewski, Piotr Stolarski, Witold Abramowicz. 453-472 [doi]
• TUDublin at CheckThat!-2023: ChatGPT for Data AugmentationElena Shushkevich, John Cardiff. 473-481 [doi]
• Z-Index at CheckThat!-2023: Unimodal and Multimodal Check-worthiness ClassificationPrerona Tarannum, Md. Arid Hasan, Firoj Alam, Sheak Rashed Haider Noori. 482-493 [doi]
• Accenture at CheckThat!-2023: Learning to Detect Factuality Levels of News SourcesSieu Tran, Paul Rodrigues 0001, Benjamin Strauss, Evan M. Williams. 494-499 [doi]
• Accenture at CheckThat!-2023: Learning to Detect Political Bias of News Articles and SourcesSieu Tran, Paul Rodrigues 0001, Benjamin Strauss, Evan M. Williams. 500-506 [doi]
• Accenture at CheckThat!-2023: Impacts of Back-translation on Subjectivity DetectionSieu Tran, Paul Rodrigues 0001, Benjamin Strauss, Evan M. Williams. 507-517 [doi]
• Accenture at CheckThat!-2023: Identifying Claims with Societal Impact using NLP Data AugmentationSieu Tran, Paul Rodrigues 0001, Benjamin Strauss, Evan M. Williams. 518-525 [doi]
• TOBB ETU at CheckThat!-2023: Utilizing ChatGPT to Detect Subjective Statements and Political BiasMehmet Deniz Türkmen, Gökalp Cosgun, Mücahid Kutlu. 526-533 [doi]
• ZHAW-CAI at CheckThat!-2023: Ensembling using Kernel AveragingPius von Däniken, Jan Deriu, Mark Cieliebak. 534-545 [doi]
• Extended Overview of DocILE 2023: Document Information Localization and ExtractionStepán Simsa, Michal Uricár, Milan Sulc, Yash Patel, Ahmed Hamdi, Matej Kocián, Matyás Skalický, Jirí Matas, Antoine Doucet, Mickaël Coustaty, Dimosthenis Karatzas. 546-571 [doi]
• RoBERTa Ensemble Technique for Document Information Localization and ExtractionBao Gia Tran, Duy-Ngo Minh Bao, Khanh Gia Bui, Huy Viet Duong, Dang Hai Nguyen 0002, Hieu Minh Nguyen. 572-582 [doi]
• Object Detection Pipeline Using YOLOv8 for Document Information ExtractionJakub Straka, Ivan Gruber. 583-597 [doi]
• USTC-iFLYTEK at DocILE: A Multi-modal Approach Using Domain-specific GraphDocYan Wang, Jun Du, Jiefeng Ma, Pengfei Hu 0006, Zhenrong Zhang, Jianshu Zhang. 598-610 [doi]
• Overview of eRisk at CLEF 2023: Early Risk Prediction on the Internet (Extended Overview)Javier Parapar, Patricia Martín-Rodilla, David E. Losada, Fabio Crestani. 611-638 [doi]
• NailP at eRisk 2023: Search for Symptoms of DepressionEduardo Bezerra 0002, Leonardo dos Santos, Rodolpho Nascimento, Rui Pedro Lopes, Gustavo Paiva Guedes. 639-661 [doi]
• Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social MediaAna-Maria Bucur. 662-671 [doi]
• NLP-UNED-2 at eRisk 2023: Detecting Pathological Gambling in Social Media through Dataset Relabeling and Neural NetworksHermenegildo Fabregat, Andrés Duque, Lourdes Araujo, Juan Martínez-Romo. 672-683 [doi]
• Transformer-based Topic Modeling to Measure the Severity of Eating Disorder SymptomsDiana-Nicoleta Grigore, Ioana Pintilie. 684-692 [doi]
• Representation Exploration and Deep Learning Applied to the Early Detection of Pathological Gambling RisksXabier Larrayoz, Nuria Lebeña, Arantza Casillas, Alicia Pérez. 693-705 [doi]
• OBSER-MENH at eRisk 2023: Deep Learning-Based Approaches for Symptom Detection in Depression and Early Identification of Pathological Gambling IndicatorsJuan Martínez-Romo, Lourdes Araujo, Xabier Larrayoz, Maite Oronoz, Alicia Pérez. 706-717 [doi]
• Lightweight Methods for Early Risk DetectionDiego Maupomé, Thomas Soulas, Fanny Rancourt, Ghyslain Cantin-Savoie, Grégoire Winterstein, Sébastien Mosser 0001, Marie-Jean Meurs. 718-726 [doi]
• BFH-AMI at eRisk@CLEF 2023Ghofrane Merhbene, Alexandre R. Puttick, Mascha Kurpicz-Briki. 727-735 [doi]
• ELiRF-UPV at eRisk 2023: Early detection of pathological gambling using SVMAntonio Molina, Xinhui Huang, Lluís F. Hurtado, Ferran Pla. 736-742 [doi]
• SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language ProcessingAlba María Mármol-Romero, Flor Miriam Plaza del Arco, Arturo Montejo Ráez. 743-751 [doi]
• UMUTeam at eRisk@CLEF 2023 Shared Task: Transformer Models for Early Detection of Pathological Gambling, Depression, and Eating DisorderRonghao Pan, José Antonio García-Díaz, Rafael Valencia-García. 752-762 [doi]
• Exploring Depression Symptoms through Similarity Methods in Social Media PostsNaveen Recharla, Prasanthi Bolimera, Yash Gupta, Anand Kumar Madasamy. 763-772 [doi]
• MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media TextsFardin Ahsan Sakib, Ahnaf Atef Choudhury, Özlem Uzuner. 773-781 [doi]
• A Natural Language Processing Based Risk Prediction Framework for Pathological GamblingAbu Talha, Tanmay Basu. 782-790 [doi]
• Strategies to Harness the Transformers' Potential: UNSL at eRisk 2023Horacio Thompson, Leticia C. Cagnina, Marcelo Errecalde. 791-804 [doi]
• uOttawa at eRisk 2023: Search for Symptoms of DepressionYuxi Wang, Diana Inkpen. 805-812 [doi]
• Overview of EXIST 2023 - Learning with Disagreement for Sexism Identification and Characterization (Extended Overview)Laura Plaza, Jorge Carrillo-de-Albornoz, Roser Morante, Enrique Amigó, Julio Gonzalo, Damiano Spina, Paolo Rosso. 813-854 [doi]
• Multilingual Sexism Identification Using Contrastive LearningJason Angel, Segun Taofeek Aroyehun, Alexander F. Gelbukh. 855-861 [doi]
• Tlatlamiztli: Fine-Tuned RoBERTuito for Sexism DetectionHardik Asnani, Andrew Davis, Aaryana Rajanala, Sandra Kübler. 862-868 [doi]
• IU-NLP-JeDi: Investigating Sexism Detection in English and SpanishMatthew Buzzell, Jeremy Dickinson, Natasha Singh, Sandra Kübler. 869-877 [doi]
• AIT_FHSTP at EXIST 2023 Benchmark: Sexism Detection by Transfer Learning, Sentiment and Toxicity Embeddings and Hand-Crafted FeaturesJaqueline Böck, Mina Schütz, Daria Liakhovets, Nathanya Queby Satriani, Andreas Babic, Djordje Slijepcevic, Matthias Zeppelzauer, Alexander Schindler. 878-890 [doi]
• Sexism Identification In Social NetworksAtul Chaudhary, Ritesh Kumar. 891-900 [doi]
• I2C-UHU at CLEF-2023 EXIST task: Leveraging Ensembling Language Models to Detect Multilingual Sexism in Social MediaPablo Cordon, Jacinto Mata, Victoria Pachón, Juan Luis Domínguez. 901-907 [doi]
• When Multiple Perspectives and an Optimization Process Lead to Better Performance, an Automatic Sexism Identification on Social Media With Pretrained Transformers in a Soft Label ContextJohan Erbani, Elöd Egyed-Zsigmond, Diana Nurbakova, Pierre-Edouard Portier. 908-918 [doi]
• CLassifiers at EXIST 2023: Detecting Sexism in Spanish and English Tweets With XLM-TBerna Ilke Ersoy, Gian Radler, Sofia Carpentieri. 919-926 [doi]
• IU-Percival: Linear Models for Sexism DetectionElizabeth Gabel, Holly Redman, Daniel Swanson, Sandra Kübler. 927-936 [doi]
• UMUTeam at EXIST 2023: Sexism identification and categorisation fine-tuning Multilingual Large Language ModelsJosé Antonio García-Díaz, Ronghao Pan, Rafael Valencia-García. 937-949 [doi]
• IUEXIST: Multilingual Pre-trained Language Models for Sexism Detection on Twitter in EXIST2023Yash A. Hatekar, Muhammad S. Abdo, Snigdha Khanna, Sandra Kübler. 950-958 [doi]
• Detection of Sexism on Social Media with Multiple Simple TransformersChirayu Jhakal, Khushi Singal, Manan Suri, Divya Chaudhary, Bijendra Kumar, Ian Gorton. 959-966 [doi]
• ROH_NEIL@EXIST2023: Detecting Sexism in Tweets using Multilingual Language ModelsRohit Koonireddy, Niloofar Adel. 967-984 [doi]
• AI-UPV at EXIST 2023 - Sexism Characterization Using Large Language Models Under The Learning with Disagreement RegimeAngel Felipe Magnossão de Paula, Giulia Rizzi, Elisabetta Fersini, Damiano Spina. 985-999 [doi]
• Towards Robust Online Sexism Detection: A Multi-Model Approach with BERT, XLM-RoBERTa, and DistilBERT for EXIST 2023 TasksHadi Mohammadi, Anastasia Giachanou, Ayoub Bagheri. 1000-1011 [doi]
• Enriching Hate-Tuned Transformer-Based Embeddings with Emotions for the Categorization of SexismArianna Muti, Eleonora Mancini. 1012-1023 [doi]
• Combining Transformer Based Language Models with Socio-demographic Information for Improving Sexism Detection in Social MediaJacobo Pedrosa-Marín, Jorge Carrillo-de-Albornoz, Laura Plaza. 1024-1036 [doi]
• Leveraging MiniLMv2 Pipelines for EXIST2023Alexandru Petrescu. 1037-1043 [doi]
• LSTM-Attention Architecture for Online Bilingual Sexism DetectionSrinivasa Ravi, Siddharth Kelkar, Anand Kumar Madasamy. 1044-1059 [doi]
• ZaRa-IU-NLP at EXIST 2023 - Sexism Identification: Specialized or Generalized?Zackary Leech, Ravi Regulagedda, Sandra Kübler. 1060-1066 [doi]
• IimasGIL_NLP@EXIST2023: Unveiling Sexism on Twitter with Fine-tuned TransformersAndrea Sanchez-Urbina, Helena Gómez-Adorno, Gemma Bel Enguix, Vianey Rodríguez-Figueroa, Angela Monge-Barrera. 1067-1082 [doi]
• Efficient Multilingual Sexism Detection via Large Language Model CascadesLin Tian, Nannan Huang, Xiuzhen Zhang. 1083-1090 [doi]
• Integrating Annotator Information in Transformer Fine-tuning for Sexism DetectionMaría Estrella Vallecillo Rodríguez, Flor Miriam Plaza del Arco, Luis Alfonso Ureña López, María-Teresa Martín Valdivia, Arturo Montejo Ráez. 1091-1106 [doi]
• Leveraging GPT-2 for Automated Classification of Online Sexist ContentAdvaitha Vetagiri, Prottay Kumar Adhikary, Partha Pakray, Amitava Das. 1107-1122 [doi]
• Overview of iDPP@CLEF 2023: The Intelligent Disease Progression Prediction ChallengeGuglielmo Faggioli, Alessandro Guazzo, Stefano Marchesin 0001, Laura Menotti, Isotta Trescato, Helena Aidos, Roberto Bergamaschi, Giovanni Birolo, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Mamede de Carvalho, Giorgio Maria Di Nunzio, Piero Fariselli, Jose Manuel García Dominguez, Marta Gromicho, Enrico Longato, Sara C. Madeira, Umberto Manera, Gianmaria Silvello, Eleonora Tavazzi, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo, Nicola Ferro 0001. 1123-1164 [doi]
• Maximum Likelihood Estimation with Deep Learning for Multiple Sclerosis Progression PredictionTsvetan Asamov, Petar Ivanov, Anna Aksenova, Dimitar Taskov, Svetla Boytcheva. 1165-1185 [doi]
• Investigating the Impact of Environmental Data on ALS Prognosis with Survival AnalysisRuben Branco, Diogo F. Soares, Andreia S. Martins, Joana Barros Valente, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos. 1186-1198 [doi]
• Survival Analysis for Multiple Sclerosis: Predicting Risk of Disease WorseningRuben Branco, Joana Barros Valente, Andreia S. Martins, Diogo F. Soares, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos. 1199-1209 [doi]
• Predicting and Explaining Risk of Disease Worsening Using Temporal Features in Multiple SclerosisTommaso Mario Buonocore, Pietro Bosoni, Giovanna Nicora, Mahin Vazifehdan, Riccardo Bellazzi, Enea Parimbelli, Arianna Dagliati. 1210-1218 [doi]
• Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease ProgressionAlessandro Guazzo, Isotta Trescato, Enrico Longato, Erica Tavazzi, Martina Vettoretti, Barbara Di Camillo. 1219-1232 [doi]
• Predicting Risk of Multiple Sclerosis WorseningMarek Hanzl, Lukás Picek. 1233-1245 [doi]
• Air Pollution Profiling through Patient Stratification: Study of ALS Staging Systems Usefulness in Facilitating Data-driven Disease Subtyping and Discovery of Hazardous Ambient Air PollutantsMohamed Chiheb Karray. 1246-1271 [doi]
• Time-to-Event Interpretable Machine Learning for Multiple Sclerosis Worsening Prediction: Results from iDPP@CLEF 2023Angela Lombardi, Maria Luigia Natalia De Bonis, Giuseppe Fasano, Alessia Sportelli, Tommaso Colafiglio, Domenico Lofù, Paolo Sorino, Fedelucio Narducci, Eugenio Di Sciascio, Tommaso Di Noia. 1272-1285 [doi]
• HULAT@IDDP CLEF 2023: Intelligent Prediction of Disease Progression in Multiple Sclerosis PatientsAlberto Ramos 0005, Paloma Martínez, Israel González-Carrasco. 1286-1297 [doi]
• Multiple Sclerosis Survival Prediction Results from DSM-COMPBIO UNITOIvan Rossi, Giovanni Birolo, Piero Fariselli. 1298-1304 [doi]
• Overview of ImageCLEFmedical GANs 2023 Task - Identifying Training Data "Fingerprints" in Synthetic Biomedical Images Generated by GANs for Medical Image SecurityAlexandra-Georgiana Andrei, Ahmedkhan Radzhabov, Ioan Coman, Vassili Kovalev, Bogdan Ionescu, Henning Müller. 1305-1315 [doi]
• Overview of ImageCLEFmedical 2023 - Medical Visual Question Answering for Gastrointestinal TractSteven Hicks, Andrea M. Storås, Pål Halvorsen, Thomas de Lange, Michael Riegler 0001, Vajira Thambawita. 1316-1327 [doi]
• Overview of ImageCLEFmedical 2023 - 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. 1328-1346 [doi]
• Overview of the MEDIQA-Sum Task at ImageCLEF 2023: Summarization and Classification of Doctor-Patient ConversationsWen-wai Yim, Asma Ben Abacha, Griffin Adams, Neal Snider, Meliha Yetisgen. 1347-1360 [doi]
• Overview of ImageCLEFfusion 2023 Task - Testing Ensembling Methods in Diverse ScenariosLiviu-Daniel Stefan, Mihai Gabriel Constantin, Mihai Dogariu, Bogdan Ionescu. 1361-1366 [doi]
• TREDENCE at MEDIQA-Sum 2023: Clinical Note Generation from Doctor Patient Conversation using Utterance Segmentation and Question-Answer Driven Abstractive SummarizationVaibhav Adwani, Mohammed Sameer Khan, Ankush Chopra. 1367-1378 [doi]
• AIMultimediaLab at ImageCLEFmedical GANs 2023: Determining "Fingerprints" of Training Data in Generated Synthetic ImagesAlexandra-Georgiana Andrei, Bogdan Ionescu. 1379-1386 [doi]
• Multi-stage Medical Image Captioning using Classification and CLIPMasaki Aono, Hiroki Shinoda, Tetsuya Asakawa, Kazuki Shimizu, Takuya Togawa, Takuyuki Komoda. 1387-1395 [doi]
• Real and Generated Image Classification using Multi-stage Transfer LearningTetsuya Asakawa, Hiroki Shinoda, Takuya Togawa, Kazuki Shimizu, Masaki Aono. 1396-1402 [doi]
• Efficient Fusion Techniques for Result Diversification and Image Interestingness TasksPrabavathy Balasundaram, G. Gnana Sai, Kishore N, Olirva M, Makesh Vaibhav A. G, Naren Srinivasan Murali, Parlapalli Sai Harshith. 1403-1414 [doi]
• Correlating Biomedical Image Fingerprints between GAN-generated and Real Images using a ResNet Backbone with ML-based Downstream Comparators and Clustering: ImageCLEFmed GANs, 2023Haricharan Bharathi, Anirudh Bhaskar, Vishal Venkataramani, Karthik Desingu, Lekshmi Kalinathan. 1415-1422 [doi]
• UETCorn at MEDIQA-Sum 2023: Template-based Summarization for Clinical Note Generation from Doctor-Patient ConversationDuy-Cat Can, Quoc-An Nguyen, Binh-Nguyen Nguyen, Minh-Quang Nguyen, Khanh-Vinh Nguyen, Trung Hieu Do, Hoang-Quynh Le. 1423-1432 [doi]
• Finding the Source Images From the Generated Images with Contrastive Learning MethodsShitong Cao, Xiaobing Zhou. 1433-1439 [doi]
• StellEllaStars at MEDIQA-Sum 2023: Exploring Transformer-Based models for Dialogue2Topic ClassificationChi-Yun Chang, Jiaqi Li, Shivangi Kumar, V. G. Vinod Vydiswaran. 1440-1449 [doi]
• Language-based Colonoscopy Image Analysis with Pretrained Neural NetworksPatrycja Cieplicka, Julia Klos, Maciej Morawski, Jaroslaw Opala. 1450-1461 [doi]
• Media Interestingness Prediction in ImageCLEFfusion 2023 with Dense Architecture-based Ensemble & Scaled Gradient Boosting Regressor ModelMd. Ismail Siddiqi Emon, Md Mahmudur Rahman. 1462-1466 [doi]
• Analyzing the Similarity between Artificial and Training Images in Generative Models: The PicusLabMed ContributionMichela Gravina, Stefano Marrone 0002, Carlo Sansone. 1467-1477 [doi]
• A Dual of Stacked Attention Networks (SAN's) and VGG-16 Model-Based Visual Question Answering EvaluationRohit Raj Gunti, Abebe Rorissa. 1478-1487 [doi]
• HuskyScribe at MEDIQA-Sum 2023: Summarizing Clinical Dialogues with TransformersBin Han, Haotian Zhu, Sitong Zhou, Sofia Ahmed, Md. Mushfiqur Rahman, Fei Xia, Kevin Lybarger. 1488-1509 [doi]
• Concept Detection and Caption Prediction in ImageCLEFmedical Caption 2023 with Convolutional Neural Networks, Vision and Text-to-Text Transfer TransformersMd. Rakibul Hasan, Oyebisi Layode, Md Mahmudur Rahman 0003. 1510-1523 [doi]
• AUEB NLP Group at ImageCLEFmedical Caption 2023Panagiotis Kaliosis, Georgios Moschovis, Foivos Charalampakos, John Pavlopoulos, Ion Androutsopoulos. 1524-1548 [doi]
• SSNdhanyadivyakavitha at MEDIQA-Sum 2023: Medical Dialogue Summarization using Linear Support Vector Classification TechniqueDhanya Krishnan, Divya Srinivasan, Kavitha Srinivasan. 1549-1557 [doi]
• IUST_NLPLAB at ImageCLEFmedical Caption Tasks 2023Yasaman Lotfollahi, Melika Nobakhtian, Malihe Hajihosseini, Sauleh Eetemadi. 1558-1570 [doi]
• UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question AnsweringTriet M. Thai, Anh T. Vo, Hao K. Tieu, Linh N. P. Bui, Thien T. B. Nguyen. 1571-1587 [doi]
• GAN-ISI: Generative Adversarial Networks Image Source Identification Using Texture AnalysisMehdi Mehdipour-Ghazi, Mostafa Mehdipour-Ghazi. 1588-1595 [doi]
• Evaluating Privacy on Synthetic Images Generated using GANs: Contributions of the VCMI Team to ImageCLEFmedical GANs 2023Helena Montenegro, Pedro C. Neto, Cristiano Patrício, Isabel Rio-Torto, Tiago Gonçalves, Luís F. Teixeira 0001. 1596-1610 [doi]
• A Concise Model for Medical Image CaptioningAaron Nicolson, Jason Dowling, Bevan Koopman. 1611-1619 [doi]
• SSN MLRG at ImageCLEFmedical Caption 2023: Automatic Concept Detection and Caption Prediction using ConceptNet and Vision TransformerSheerin Sitara Noor Mohamed, Kavitha Srinivasan. 1620-1626 [doi]
• SSN MLRG at MEDIQA-SUM 2023: Automatic Text Summarization using Support Vector Machine and RoBERTaSheerin Sitara Noor Mohamed, Kavitha Srinivasan. 1627-1632 [doi]
• SSN MLRG at MEDVQA-GI 2023: Visual Question Generation and Answering using Transformer based Pre-trained ModelsSheerin Sitara Noor Mohamed, Kavitha Srinivasan, Raghuraman Gopalsamy. 1633-1640 [doi]
• Concept Detection and Caption Prediction from Medical Images using Gradient Boosted Ensembles and Deep LearningMirunalini Palaniappan, Haricharan Bharathi, Eeswara Anvesh Chodisetty, Anirudh Bhaskar, Karthik Desingu. 1641-1652 [doi]
• Detecting Concepts and Generating Captions from Medical Images: Contributions of the VCMI Team to ImageCLEFmedical Caption 2023Isabel Rio-Torto, Cristiano Patrício, Helena Montenegro, Tiago Gonçalves, Jaime S. Cardoso 0001. 1653-1667 [doi]
• PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical RecordsViktor Schlegel, Hao Li, Yu Ping Wu, Anand Subramanian 0004, Thanh Tung Nguyen, Abhinav Ramesh Kashyap, Daniel Beck, Xiao-Jun Zeng, Riza Theresa Batista-Navarro, Stefan Winkler 0001, Goran Nenadic. 1668-1679 [doi]
• Team Cadence at MEDIQA-Sum 2023: Using ChatGPT as a Data Augmentation Tool for Classifying Clinical DialogueAshwyn Sharma, David I. Feldman. 1680-1687 [doi]
• KDE Lab at ImageCLEFmedical Caption 2023Hiroki Shinoda, Masaki Aono, Tetsuya Asakawa, Kazuki Shimizu, Takuyuki Komoda, Takuya Togawa. 1688-1701 [doi]
• DMK-SSN at ImageCLEF 2023 Medical: Controlling the Quality of Synthetic Medical Images Created via GANs using Machine Learning and Image Hashing TechniquesDhivya Subburam, Shriram M. SathyaNarayanan, Bhavana Anand, Kavitha Srinivasan, Mohanavalli Subramaniam. 1702-1710 [doi]
• MLRG-JBTTM at MEDIQA-Sum 2023: Dialogue2Topic ClassificationHarshida Sujatha Palaniraj, Keerthan Vinod, Mohith Adluru, Bhuvana Jayaraman, Mirnalinee T. T. 1711-1719 [doi]
• SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue SummarizationKunal Suri, Prakhar Mishra, Saumajit Saha, Atul Singh. 1720-1735 [doi]
• BIT Mesra at ImageCLEF 2023: Fusion of Blended Image and Text Features for Medical VQASushmita Upadhyay, Sanjaya Shankar Tripathy. 1736-1743 [doi]
• Adapting Pre-Trained Visual and Language Models for Medical Image Question AnsweringSiqi Wang, Wenshuo Zhou, Yehui Yang, Haifeng Huang, ZhiYu Ye, Tong Zhang, Dalu Yang. 1744-1753 [doi]
• PCLmed at ImageCLEFmedical 2023: Customizing General-Purpose Foundation Models for Medical Report GenerationBang Yang, Asif Raza, Yuexian Zou, Tong Zhang. 1754-1766 [doi]
• Concept Detection and Image Caption Generation in Medical ImagingVarsha Yeshwanth, Pranith P, Lekshmi Kalinathan. 1767-1775 [doi]
• Transferring Pre-Trained Large Language-Image Model for Medical Image CaptioningWenshuo Zhou, ZhiYu Ye, Yehui Yang, Siqi Wang, Haifeng Huang, Rongjie Wang, Dalu Yang. 1776-1784 [doi]
• Overview of JOKER 2023 Automatic Wordplay Analysis Task 1 - Pun DetectionLiana Ermakova, Tristan Miller, Anne-Gwenn Bosser, Victor Manuel Palma-Preciado, Grigori Sidorov, Adam Jatowt. 1785-1803 [doi]
• Overview of JOKER 2023 Automatic Wordplay Analysis Task 2 - Pun Location and InterpretationLiana Ermakova, Tristan Miller, Anne-Gwenn Bosser, Victor Manuel Palma-Preciado, Grigori Sidorov, Adam Jatowt. 1804-1817 [doi]
• Overview of JOKER 2023 Automatic Wordplay Analysis Task 3 - Pun TranslationLiana Ermakova, Tristan Miller, Anne-Gwenn Bosser, Victor Manuel Palma-Preciado, Grigori Sidorov, Adam Jatowt. 1818-1827 [doi]
• Exploring Humor in Natural Language Processing: A Comprehensive Review of JOKER Tasks at CLEF Symposium 2023Aftab Anjum, Nikolaus Lieberum. 1828-1837 [doi]
• CLEF 2023 JOKER Task 2 : Using Chat GPT for Pun Location and InterpretationOcéane Brunelière, Constance Germann, Keith Salina. 1838-1845 [doi]
• AKRaNLU @ CLEF JOKER 2023: Using Sentence Embeddings and Multilingual Models to Detect and Interpret WordplayRyan Rony Dsilva. 1846-1853 [doi]
• UBO Team @ CLEF JOKER 2023 Track For Task 1, 2 and 3 - Applying AI Models In Regards To Pun TranslationQuentin Dubreuil. 1854-1861 [doi]
• BU-Pier Team @ CLEF JOKER 2023 Open Task: Slip of the Tongue Generation to Improve Social Interaction with Virtual AgentsLoïc Glemarec, Fred Charles. 1862-1867 [doi]
• CLEF2023' JOKERJulia Komorowska, Iva Catipovic, Darko Vujica. 1868-1874 [doi]
• CLEF 2023 JOKER Tasks 2 and 3: Using NLP Models for Pun Location, Interpretation and TranslationFelix Ohnesorge, Mari Ángeles Gutiérrez, Julia Plichta. 1875-1880 [doi]
• NLPalma @ CLEF 2023 JOKER: A BLOOMZ and BERT Approach for Wordplay Detection and TranslationVictor Manuel Palma-Preciado, Carolina Palma Preciado, Grigori Sidorov. 1881-1887 [doi]
• Does AI Have a Sense of Humor? CLEF 2023 JOKER Tasks 1, 2 and 3: Using BLOOM, GPT, SimpleT5, and More for Pun Detection, Location, Interpretation and TranslationOlga Popova, Petra Dadic. 1888-1908 [doi]
• CLEF 2023 JOKER Task 1, 2, 3: Pun Detection, Pun Interpretation, and Pun TranslationAntonela Prnjak, Dennis R. Davari, Kristina Schmitt. 1909-1917 [doi]
• Innsbruck @ JOKER2023 Task 1: Data Augmentation Techniques for Humor Recognition in TextStefan Reicho, Adam Jatowt. 1918-1924 [doi]
• Why Sentiment Analysis is a Joke with JOKER data? Word-Level and Interpretation Analysis (CLEF 2023 JOKER Task 2)Tremaine Thomas-Young. 1925-1933 [doi]
• Overview of BirdCLEF 2023: Automated Bird Species Identification in Eastern AfricaStefan Kahl, Tom Denton, Holger Klinck, Hendrik Reers, Francis Cherutich, Hervé Glotin, Hervé Goëau, Willem-Pier Vellinga, Robert Planqué, Alexis Joly. 1934-1942 [doi]
• Overview of FungiCLEF 2023: Fungi Recognition Beyond 1/0 CostLukás Picek, Milan Sulc, Rail Chamidullin, Jirí Matas. 1943-1953 [doi]
• Overview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution at Continental Scale Using Remote SensingChristophe Botella, Benjamin Deneu, Diego Marcos Gonzalez, Maximilien Servajean, Théo Larcher, César Leblanc, Joaquim Estopinan, Pierre Bonnet, Alexis Joly. 1954-1971 [doi]
• Overview of PlantCLEF 2023: Image-based Plant Identification at Global ScaleHervé Goëau, Pierre Bonnet, Alexis Joly. 1972-1981 [doi]
• Overview of SnakeCLEF 2023: Snake Identification in Medically Important ScenariosLukás Picek, Rail Chamidullin, Marek Hrúz, Andrew M. Durso. 1982-1995 [doi]
• Metric-Weighted Ensemble Focal Loss for Snake Species IdentificationAarti Balana. 1996-2006 [doi]
• Joint Feature Learning of Image Data with Embedded Metadata to Leverage Snake Species ClassificationBenjamin Bracke, Mohammadreza Bagherifar, Louise Bloch, Christoph M. Friedrich. 2007-2034 [doi]
• Deep Learning for Large-Scale Plant Classification: NEUON Submission to PlantCLEF 2023Sophia Chulif, Yang Loong Chang, Sue Han Lee. 2035-2042 [doi]
• Acoustic Bird Species Recognition at BirdCLEF 2023: Training Strategies for Convolutional Neural Network and Inference Acceleration using OpenVINOLihang Hong. 2043-2050 [doi]
• A Deep Learning based Solution to FungiCLEF2023Feiran Hu, Peng Wang, Yangyang Li, Chenlong Duan, Zijian Zhu, Yong Li, Xiu-Shen Wei. 2051-2059 [doi]
• Watch out Venomous Snake Species: A Solution to SnakeCLEF2023Feiran Hu, Peng Wang, Yangyang Li, Chenlong Duan, Zijian Zhu, Fei Wang, Faen Zhang, Yong Li, Xiu-Shen Wei. 2060-2070 [doi]
• Bird Species Recognition using Convolutional Neural Networks with Attention on Frequency BandsMario Lasseck. 2071-2079 [doi]
• Classic Approaches to Bird Song ClassificationMihai-Dimitrie Minut, Cristian Simionescu, Adrian Iftene. 2080-2090 [doi]
• Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall ClassificationAnthony Miyaguchi, Nathan Zhong, Murilo Gustineli, Chris Hayduk. 2091-2106 [doi]
• Reading the Robot Mind - Presenting Internal Data Flow Within an AI for Classification of Bird Sounds in a Format Familiar to Subject Matter ExpertsPaul Nussbaum. 2107-2121 [doi]
• Entropy-guided Open-set Fine-grained Fungi RecognitionHuan Ren, Han Jiang, Wang Luo, Meng Meng, Tianzhu Zhang. 2122-2136 [doi]
• Metaformer Model with ArcFaceLoss and Contrastive Learning for SnakeCLEF2023 Fine-Grained ClassificationZhennan Shi, Huazhen Chen, Chang Liu, Jun Qiu. 2137-2148 [doi]
• Leverage Samples with Single Positive Labels to Train CNN-based Models For Multi-label Plant Species PredictionHuy Quang Ung, Ryoichi Kojima, Shinya Wada. 2149-2158 [doi]
• Optimizing Fine-Grained Fungi Classification for Diverse Application-Oriented Open-Set MetricsStefan Wolf, Jürgen Beyerer. 2159-2167 [doi]
• PlantCLEF2023: A Bigger Training Dataset Contributes More than Advanced Pretraining Methods for Plant IdentificationMingle Xu, Sook Yoon, Chenmou Wu, Jeonghyun Baek, Dong-Sun Park. 2168-2180 [doi]
• Extended Overview of the CLEF-2023 LongEval Lab on Longitudinal Evaluation of Model PerformanceRabab Alkhalifa, Iman Munire Bilal, Hsuvas Borkakoty, José Camacho-Collados, Romain Deveaud, Alaa El-Ebshihy, Luis Espinosa Anke, Gabriela Nicole González Sáez, Petra Galuscáková, Lorraine Goeuriot, Elena Kochkina, Maria Liakata, Daniel Loureiro, Philippe Mulhem, Florina Piroi, Martin Popel, Christophe Servan, Harish Tayyar Madabushi, Arkaitz Zubiaga. 2181-2203 [doi]
• SEUNIPD@CLEF: Team JIHUMING on Enhancing Search Engine Performance with Character N-Grams, Query Expansion, and Named Entity RecognitionIsil Atabek, Huimin Chen, Jesús Moncada-Ramírez, Nicolò Santini, Giovanni Zago, Nicola Ferro 0001. 2204-2221 [doi]
• SEUPD@CLEF: Team GWCA on Longitudinal Evaluation of IR Systems by Using Query Expansion and Learning To RankLeonardo Bellin, Antonino Andrea Carè, Marco Martini, Maria Teresa Pepaj, Matteo Salvalaio, Andrea Segala, Mariafiore Tognon, Nicola Ferro 0001. 2222-2238 [doi]
• SEUPD@CLEF: RAFJAM on Longitudinal Evaluation of Model PerformanceAlvise Bolzonella, Riccardo Broetto, Marco Gasparini, Farhad Sadat, Nicola Ferro 0001. 2239-2251 [doi]
• SEUPD@CLEF: Team FADERIC on A Query Expansion and Reranking Approach for the LongEval TaskEnrico Bolzonello, Christian Marchiori, Daniele Moschetta, Riccardo Trevisiol, Fabio Zanini, Nicola Ferro 0001. 2252-2280 [doi]
• SEUPD@CLEF: Team NEON. A Memoryless Approach To Longitudinal EvaluationSimone Bortolin, Gioele Ceccon, Gil Czaczkes, Alessandra Pastore, Pietro Renna, Giovanni Zerbo, Nicola Ferro 0001. 2281-2305 [doi]
• SEUPD@CLEF: Team Squid on LongEval-RetrievalVittorio Cardillo, Alberto Dorizza, Mattia Maglie, Dario Mameli, Gianluca Rossi, Michele Russo, Nicola Ferro 0001. 2306-2337 [doi]
• SEUPD@CLEF: Team DARDS - IR System for Short and Long Term RetrievalDaniel Carlesso, Riccardo Gobbo, Simone Merlo, Angela Pomaro, Diego Spinosa, Nicola Ferro 0001. 2338-2367 [doi]
• SEUPD@CLEF: Team CLOSE on Temporal Persistence of IR Systems' PerformanceGianluca Antolini, Nicola Boscolo, Mirco Cazzaro, Marco Martinelli, Seyedreza Safavi, Farzad Shami, Nicola Ferro 0001. 2368-2395 [doi]
• SEUPD@CLEF: Team HIBALL on Incremental Information Retrieval System with RRF and BERTAndrea Ceccato, Luca Fabbian, Bor-Woei Huang, Irfan Ullah Khan 0003, Harjot Singh, Nicola Ferro 0001. 2396-2415 [doi]
• SEUPD@CLEF: Team QEVALS on Information Retrieval Adapted to the Temporal Evolution of Web DocumentsEnrico D'Alberton, Saverio Fincato, Vaidas Lenartavicius, Laura Pallante, Yi Jian Qiu, Nicola Ferro 0001. 2416-2431 [doi]
• Open Web Search at LongEval 2023: Reciprocal Rank Fusion on Automatically Generated Query VariantsMaik Fröbe, Gijs Hendriksen, Arjen P. de Vries, Martin Potthast. 2432-2440 [doi]
• Evaluating Temporal Persistence Using Replicability MeasuresJüri Keller, Timo Breuer 0002, Philipp Schaer. 2441-2457 [doi]
• The Temporal Persistence of Generative Language Models in Sentiment AnalysisPablo Medina-Alias, Özgür Simsek. 2458-2468 [doi]
• Keeping in Time: Adding Temporal Context to Sentiment Analysis ModelsDean Ninalga. 2469-2475 [doi]
• Overview of the Authorship Verification Task at PAN 2023Efstathios Stamatatos, Krzysztof Kredens, Piotr Pezik, Annina Heini, Janek Bevendorff, Benno Stein 0001, Martin Potthast. 2476-2491 [doi]
• Profiling Cryptocurrency Influencers with Few-shot LearningMara Chinea-Rios, Ian Borrego-Obrador, Marc Franco-Salvador, Francisco Rangel, Paolo Rosso. 2492-2512 [doi]
• Overview of the Multi-Author Writing Style Analysis Task at PAN 2023Eva Zangerle, Maximilian Mayerl, Martin Potthast, Benno Stein 0001. 2513-2522 [doi]
• Overview of the Trigger Detection Task at PAN 2023Matti Wiegmann, Magdalena Wolska, Martin Potthast, Benno Stein 0001. 2523-2536 [doi]
• Leveraging Large Language? Models with Multiple Loss Learners for Few-Shot Author ProfilingHamed Babaei Giglou, Mostafa Rahgouy, Jennifer D'Souza 0001, Milad Molazadeh Oskuee, Hadi Bayrami Asl Tekanlou, Cheryl D. Seals. 2539-2551 [doi]
• A Dual-model Classification Based on RoBERTa for Trigger DetectionGuiyuan Cao, Zhongyuan Han, Haojie Cao, Ximin Huang, Zhengqiao Zeng, Yaozu Tan, Jiyin Cai, Xu Sun. 2552-2556 [doi]
• Trigger Warning Labeling with RoBERTa and Resampling for Distressing Content DetectionHaojie Cao, Zhongyuan Han, Guiyuan Cao, Ruihao Zhu, Yongqi Liang, Siman Liu, Minhua Huang, Haihao Yu. 2557-2561 [doi]
• A Writing Style Embedding Based on Contrastive Learning for Multi-Author Writing Style AnalysisHaoyang Chen, Zhongyuan Han, Zengyao Li, Yong Han. 2562-2567 [doi]
• Using BERT to Profiling Cryptocurrency InfluencersDaniel Yacob Espinosa, Grigori Sidorov. 2568-2573 [doi]
• FoSIL at PAN'23: Trigger Detection with a Two Stage Topic ClassifierJenny Felser, Christoph Demus, Dirk Labudde, Michael Spranger. 2574-2587 [doi]
• Profiling Cryptocurrency Influencers with Few-shot LearningIsabel Ferri-Molla, Jaume Santamaria-Jorda. 2588-2598 [doi]
• Profiling Cryptocurrency Influencers with Sentence TransformersKavya Girish, Asha Hegde, Fazlourrahman Balouchzahi, Hosahalli Lakshmaiah Shashirekha. 2599-2607 [doi]
• A Contrastive Learning of Sample Pairs for Authorship VerificationMingcan Guo, Zhongyuan Han, Haoyang Chen, Haoliang Qi. 2608-2612 [doi]
• Enhancing Writing Style Change Detection using Transformer-based Models and Data AugmentationAhmad Hashemi, Wei Shi. 2613-2621 [doi]
• Trigger Detection in Social Media TextAsha Hegde, Fazlourrahman Balouchzahi, Kavya Girish, Hosahalli Lakshmaiah Shashirekha. 2622-2628 [doi]
• Encoded Classifier Using Knowledge Distillation for Multi-Author Writing Style AnalysisMingjie Huang, Zhaohao Huang, Leilei Kong. 2629-2634 [doi]
• Authorship Verification Based on CoSENTZhaohao Huang, Leilei Kong, Mingjie Huang. 2635-2639 [doi]
• Enhancing Authorship Verification using Sentence-TransformersMomen Ibrahim, Ahmed Akram, Mohammed Radwan, Rana Ayman, Mustafa Abd-El-Hameed, Nagwa M. El-Makky, Marwan Torki. 2640-2651 [doi]
• Authorship Verification Machine Learning Methods For Style Change Detection In TextsGianni X. Jacobo, Valeria Dehesa-Corona, Ariel D. Rojas-Reyes, Helena Gómez-Adorno. 2652-2658 [doi]
• ARC-NLP at PAN 2023: Transition-Focused Natural Language Inference for Writing Style DetectionIzzet Emre Kucukkaya, Umitcan Sahin, Cagri Toraman. 2659-2668 [doi]
• UZH at PAN-2023: Profiling Cryptocurrency Influencers using Ensemble of Language ModelsAbhinav Kumar, Le Hoang Minh Trinh, Afshan Anam Saeed. 2669-2678 [doi]
• Reshape or Update? Metric Learning and Fine-tuning for Low-Resource Influencer ProfilingRoberto Labadie Tamayo, Areg Mikael Sarvazyan. 2679-2690 [doi]
• Author Verification Of Text Fragments Based On The Bert ModelJi Li, Qianjin Zhang, Mingjie Huang. 2691-2694 [doi]
• Profiling Cryptocurrency Influencers With Few-shot Learning via Contrastive LearningZengyao Li, Zhongyuan Han, Jiyin Cai, Zhijian Huang, Shaohua Huang, Leilei Kong. 2695-2701 [doi]
• Text-Segment Interaction for Authorship Verification using BERT-based ClassificationXurong Liu, Leilei Kong, Mingjie Huang. 2702-2707 [doi]
• Text Enrichment with Japanese Language to Profile Cryptocurrency InfluencersFrancesco Lomonaco, Marco Siino, Maurizio Tesconi. 2708-2716 [doi]
• Application of R-Drop in Authorship VerificationJiajun Lv, Yong Han, Qian Dong. 2717-2721 [doi]
• Profiling Cryptocurrency Influencers using Few-shot LearningHamna Muslihuddeen, Pallapothula Sathvika, Shalaka Sankar, Shreya Ostwal, Anand Kumar M. 2722-2733 [doi]
• Contrastive Learning for Authorship Verification using BERT and Bi-LSTM in a Siamese ArchitecturePanagiotis Petropoulos. 2734-2741 [doi]
• Authorship Verification Based on SimCSEYong Qiu, Haoliang Qi, Yong Han, Kaicheng Huang. 2742-2746 [doi]
• ARC-NLP at PAN 2023: Hierarchical Long Text Classification for Trigger DetectionUmitcan Sahin, Izzet Emre Kucukkaya, Cagri Toraman. 2747-2757 [doi]
• A Multi-Feature Custom Classification Approach to Authorship VerificationRiya Sanjesh, Alamelu Mangai. 2758-2762 [doi]
• XLNet with Data Augmentation to Profile Cryptocurrency InfluencersMarco Siino, Ilenia Tinnirello. 2763-2771 [doi]
• Profiling Cryptocurrency Influencers with Few-Shot Learning Using Data Augmentation and ELECTRAMarco Siino, Maurizio Tesconi, Ilenia Tinnirello. 2772-2781 [doi]
• Siamese Networks in Trigger Detection TaskYunsen Su, Yong Han, Haoliang Qi. 2782-2786 [doi]
• Stylometric and Neural Features Combined Deep Bayesian Classifier for Authorship VerificationYitao Sun, Svetlana Afanaseva, Kailash Patil. 2787-2794 [doi]
• Heterogeneous-Graph Convolutional Network for Authorship VerificationAndric Valdez-Valenzuela, Jorge Alfonso Martinez Galicia, Helena Gómez-Adorno. 2795-2802 [doi]
• Few Shot Profiling of Cryptocurrency Influencers using Natural Language Inference & Large Language ModelsEmilio Villa-Cueva, Jorge Miguel Valles-Silva, Adrián Pastor López-Monroy, Fernando Sánchez-Vega, Jesus Roberto López-Santillán. 2803-2816 [doi]
• Supervised Contrastive Learning for Multi-Author Writing Style AnalysisZhanhong Ye, Changle Zhong, Haoliang Qi, Yong Han. 2817-2822 [doi]
• Overview of the CLEF 2023 SimpleText Task 1: Passage Selection for a Simplified SummaryEric SanJuan, Stéphane Huet, Jaap Kamps, Liana Ermakova. 2823-2834 [doi]
• Overview of the CLEF 2023 SimpleText Task 2: Difficult Concept Identification and ExplanationLiana Ermakova, Hosein Azarbonyad, Sarah Bertin, Olivier Augereau. 2835-2854 [doi]
• Overview of the CLEF 2023 SimpleText Task 3: Simplification of Scientific TextsLiana Ermakova, Sarah Bertin, Helen McCombie, Jaap Kamps. 2855-2875 [doi]
• UZH_Pandas at SimpleText@CLEF-2023: Alpaca LoRA 7B and LENS Model Selection for Scientific Literature SimplificationPascal Severin Andermatt, Tobias Fankhauser. 2876-2898 [doi]
• Automatic Simplification of Scientific Texts using Pre-trained Language Models: A Comparative Study at CLEF Symposium 2023Aftab Anjum, Nikolaus Lieberum. 2899-2907 [doi]
• Comparing ChatGPT's and Human Evaluation of Scientific Texts' Translations from English to Portuguese Using Popular Automated TranslatorsSílvia Araújo, Micaela Aguiar. 2908-2917 [doi]
• Scientific Simplification, the Limits of ChatGPTSarah Bertin. 2918-2922 [doi]
• Elsevier at SimpleText: Passage Retrieval by Fine-tuning GPL on Scientific DocumentsArtemis Capari, Hosein Azarbonyad, Georgios Tsatsaronis, Zubair Afzal. 2923-2934 [doi]
• CLEF 2023 SimpletText Tasks 2 and 3: Enhancing Language Comprehension: Addressing Difficult Concepts and Simplifying Scientific Texts Using GPT, BLOOM, KeyBert, Simple T5 and MorePetra Dadic, Olga Popova. 2935-2957 [doi]
• CLEF2023 SimpleText Task 2, 3: Identification and Simplification of Difficult TermsDennis R. Davari, Antonela Prnjak, Kristina Schmitt. 2958-2968 [doi]
• UBO Team @ CLEF SimpleText 2023 Track for Task 2 and 3 - Using IA models to simplify Scientific TextsQuentin Dubreuil. 2969-2978 [doi]
• Domain Context-centered Retrieval for the Content Selection task in the Simplification of Scientific LiteratureÓscar E. Mendoza, Gabriella Pasi. 2979-2986 [doi]
• Text Simplification of Scientific Texts for Non-Expert ReadersBjörn Engelmann 0002, Fabian Haak, Christin Katharina Kreutz, Narjes Nikzad-Khasmakhi, Philipp Schaer. 2987-2998 [doi]
• An Evaluation of MUSS and T5 Models in Scientific Sentence Simplification: A Comparative StudyRunning Hou, Xinyi Qin. 2999-3006 [doi]
• University of Amsterdam at the CLEF 2023 SimpleText TrackRoos Hutter, Jop Sutmuller, Mary Adib, David Rau, Jaap Kamps. 3007-3016 [doi]
• AIIR and LIAAD Labs Systems for CLEF 2023 SimpleTextBehrooz Mansouri, Shea Durgin, Sj Franklin, Sean Fletcher, Ricardo Campos 0001. 3017-3026 [doi]
• CLEF 2023: Scientific Text Simplification and General AudienceFelix Ohnesorge, Mari Ángeles Gutiérrez, Julia Plichta. 3027-3032 [doi]
• SINAI Participation in SimpleText Task 2 at CLEF 2023: GPT-3 in Lexical Complexity Prediction for General AudienceJenny Ortiz-Zambrano, César Espin-Riofrio, Arturo Montejo Ráez. 3033-3044 [doi]
• NLPalma @ CLEF 2023 SimpleText: BLOOMZ and BERT for Complexity and Simplification TaskVictor Manuel Palma-Preciado, Carolina Palma Preciado, Grigori Sidorov. 3045-3049 [doi]
• CLEF2023 SimpleTextDarko Vujica, Iva Catipovic, Julia Komorowska. 3050-3056 [doi]
• A Prompt Engineering Approach to Scientific Text Simplification: CYUT at SimpleText2023 Task3Shih-Hung Wu, Hong-Yi Huang. 3057-3064 [doi]
• Overview of Touché 2023: Argument and Causal RetrievalAlexander Bondarenko 0001, Maik Fröbe, Johannes Kiesel, Ferdinand Schlatt, Valentin Barrière, Brian Ravenet, Léo Hemamou, Simon Luck, Jan Heinrich Reimer, Benno Stein 0001, Martin Potthast, Matthias Hagen. 3065-3089 [doi]
• Silver Surfer team at Touché task 4: Testing Data Augmentation and Label Propagation for Multilingual Stance DetectionJorge Avila, Álvaro Rodrigo, Roberto Centeno. 3090-3097 [doi]
• Neville Longbottom at Touché 2023: Image Retrieval for Arguments using ChatGPT, CLIP and IBM DebaterDaria Elagina, Bernd-Albrecht Heizmann, Max Koch, Gustav Lahmann, Christian Ortlepp. 3098-3103 [doi]
• Evidence Retrieval for Causal Questions Using Query Expansion And RerankingAron Gaden, Niklas Rausch, Bruno Reinhold, Lukas Zeit-Altpeter. 3104-3110 [doi]
• Jean-Luc Picard at Touché 2023: Comparing Image Generation, Stance Detection and Feature Matching for Image Retrieval for ArgumentsMax Moebius, Maximilian Enderling, Sarah T. Bachinger. 3111-3118 [doi]
• Argument Quality Prediction for Ranking DocumentsMoritz Plenz, Raphael Buchmüller, Alexander Bondarenko 0001. 3119-3130 [doi]
• Queen of Swords at Touché 2023: Intra-Multilingual Multi-Target Stance Classification using BERTKarla Schäfer. 3131-3138 [doi]