TY - CHAP A1 - Struß, Julia Maria A1 - Siegel, Melanie A1 - Ruppenhofer, Josef A1 - Wiegand, Michael A1 - Klenner, Manfred T1 - Overview of GermEval Task 2, 2019 shared task on the identification of offensive language T2 - Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019) N2 - We present the second edition of the GermEval Shared Task on the Identification of Offensive Language. This shared task deals with the classification of German tweets from Twitter. Two subtasks were continued from the first edition, namely a coarse-grained binary classification task and a fine-grained multi-class classification task. As a novel subtask, we introduce the classification of offensive tweets as explicit or implicit. The shared task had 13 participating groups submitting 28 runs for the coarse-grained task, another 28 runs for the fine-grained task, and 17 runs for the implicit-explicit task. We evaluate the results of the systems submitted to the shared task. The shared task homepage can be found at https://projects.fzai.h-da.de/iggsa/ KW - Automatische Spracherkennung KW - Beleidigung KW - Deutsch KW - Social Media KW - Tweet KW - Twitter Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:525-26209 UR - https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/germeval/GermEvalSharedTask2019Iggsa.pdf SP - 352 EP - 363 PB - German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg CY - München ER - TY - CHAP A1 - Siegel, Melanie ED - Büttner, Stephan T1 - Von Informationswissenschaft zu Information Science T2 - Die digitale Transformation in Institutionen des kulturellen Gedächtnisses : Antworten aus der Informationswissenschaft N2 - Das Fach Informationswissenschaft ist aktueller denn je. Die Absolventinnen und Absolventen werden in der Industrie nachgefragt, es gibt praktisch keine Arbeitslosigkeit. Auch die Nachfrage bei den Studienanfängern ist stabil auf hohem Niveau. Andererseits ist das Fach einem starken Wandel unterzogen. Die Inhalte werden technischer, internationaler und forschender. Den Wandel tragen die neue Generation der Studierenden und die Professorinnen und Professoren. Um diese Änderungen deutlich zu machen, haben wir den Studiengang in Darmstadt in „Information Science“ umbenannt. KW - Informations- und Dokumentationswissenschaft KW - Hochschule Darmstadt KW - Hochschuldidaktik KW - Entdeckendes Lernen KW - Internationalisierung Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:525-24096 SP - 13 EP - 24 PB - Simon Verlag für Bibliothekswissen CY - Berlin ER - TY - CHAP A1 - Nakov, Preslav A1 - Barrón-Cedño, Alberto A1 - Da San Martino, Giovanni A1 - Alam, Firoj A1 - Struß, Julia Maria A1 - Mandl, Thomas A1 - Míguez, Rubén A1 - Caselli, Tommaso A1 - Kutlu, Mucahid A1 - Zaghouani, Wajdi A1 - Li, Chengkai A1 - Shaar, Shaden A1 - Shahi, Gautam Kishore A1 - Mubarak, Hamdy A1 - Nikolov, Alex A1 - Babulkov, Nikolay A1 - Kartal, Yavuz Selim A1 - Wiegand, Michael A1 - Siegel, Melanie A1 - Köhler, Juliane T1 - Overview of the CLEF-2022 CheckThat! Lab on Fighting the COVID-19 Infodemic and Fake News Detection T2 - Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. N2 - We describe the fifth edition of the CheckThat! lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality in multiple languages: Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Task 1 asks to identify relevant claims in tweets in terms of check-worthiness, verifiability, harmfullness, and attention-worthiness. Task 2 asks to detect previously fact-checked claims that could be relevant to fact-check a new claim. It targets both tweets and political debates/speeches. Task 3 asks to predict the veracity of the main claim in a news article. CheckThat! was the most popular lab at CLEF-2022 in terms of team registrations: 137 teams. More than one-third (37%) of them actually participated: 18, 7, and 26 teams submitted 210, 37, and 126 official runs for tasks 1, 2, and 3, respectively. KW - Check-worthiness estimation KW - Fact-checking KW - Desinformation KW - Fehlinformation KW - COVID-19 Y1 - 2022 SN - 978-3-031-13643-6 U6 - https://doi.org/10.1007/978-3-031-13643-6_29 SN - 0302-9743 SP - 495 EP - 520 PB - Springer CY - Cham ER - TY - CHAP A1 - Galassi, Andrea A1 - Ruggeri, Federico A1 - Barrón-Cedeño, Alberto A1 - Alam, Firoj A1 - Caselli, Tommaso A1 - Kutlu, Mucahid A1 - Struß, Julia Maria A1 - Antici, Francesco A1 - Hasanain, Maram A1 - Köhler, Juliane A1 - Korre, Katerina A1 - Leistra, Folkert A1 - Muti, Arianna A1 - Siegel, Melanie A1 - Türkmen, Mehmet Deniz A1 - Wiegand, Michael A1 - Zaghouani, Wajdi ED - Aliannejadi, Mohammad ED - Faggiolo, Guglielmo ED - Ferro, Nicola ED - Vlachos, Michalis T1 - Overview of the CLEF-2023 CheckThat! Lab: Task 2 on Subjectivity in News Articles BT - Notebook for the CheckThat! Lab at CLEF 2023 T2 - CLEF 2023 Working Notes N2 - We describe the outcome of the 2023 edition of the CheckThat!Lab at CLEF. We focus on subjectivity (Task 2), which has been proposed for the first time. It aims at fostering the technology for the identification of subjective text fragments in news articles. For that, we produced corpora consisting of 9,530 manually-annotated sentences, covering six languages - Arabic, Dutch, English, German, Italian, and Turkish. Task 2 attracted 12 teams, which submitted a total of 40 final runs covering all languages. The most successful approaches addressed the task using state-of-the-art multilingual transformer models, which were fine-tuned on language-specific data. Teams also experimented with a rich set of other neural architectures, including foundation models, zero-shot classifiers, and standard transformers, mainly coupled with data augmentation and multilingual training strategies to address class imbalance. We publicly release all the datasets and evaluation scripts, with the purpose of promoting further research on this topic. KW - Desinformation KW - Fehlinformation KW - COVID-19 Y1 - 2023 UR - https://ceur-ws.org/Vol-3497/paper-020.pdf SP - 236 EP - 249 CY - Thessaloniki ER -