> Call for workshop papers and Shared Task participation: the 6th workshop > on Challenges and Applications of Automated Extraction of Socio-political > Events from Text - CASE @ RANLP 2023 > > > ************************************************************************************ > > URL: https://emw.ku.edu.tr/case-2023/ > > Paper submission deadline: 10 July 2023 > > Paper acceptance notification: 5 August 2023 > > Paper camera-ready: 25 August 2023 > > Workshop dates: 7-8 September 2023 > > Dates and deadlines for the shared task are below. > > Softconf page of the workshop: https://softconf.com/ranlp23/CASE/ > > > ************************************************************************************ > > We invite contributions from researchers in computer science, NLP, ML, DL, > AI, socio-political sciences, conflict analysis and forecasting, peace > studies, as well as computational social science scholars involved in the > collection and utilization of socio-political event data. This includes > (but is not limited to) the following topics > > 1) Extracting events and their arguments such as time and location in and > beyond a sentence or document, event coreference resolution. > > 2) Research in NLP technologies in relation to event detection: geocoding, > temporal reasoning, argument structure detection, syntactic and semantic > analysis of event structures, text classification, for event type > detection, learning event-related lexica, event co-reference resolution, > fake news analysis, and others with a focus on real or potential event > detection applications. > > 3) New datasets, training data collection, and annotation for event > information. > > 4) Event-event relations, e.g., subevents, main events, spatio-temporal > relations, causal relations. > > 5) Event dataset evaluation in light of reliability and validity metrics. > > 6) Defining, populating, and facilitating event schemas and ontologies. > > 7) Automated tools and pipelines for event collection related tasks. > > 8) Lexical, syntactic, semantic, discursive, and pragmatic aspects of > event manifestation. > > 9) Methodologies for development, evaluation, and analysis of event > datasets. > > 10) Applications of event databases, e.g. early warning, conflict > prediction, policymaking. > > 11) Estimating what is missing in event datasets using internal and > external information. > > 12) Detection of new and emerging SPE types, e.g. creative protests. > > 13) Release of new event datasets. > > 14) Bias and fairness of the sources and event datasets. > > 15) Ethics, misinformation, privacy, and fairness concerns pertaining to > event datasets. > > 16) Copyright issues on event dataset creation, dissemination, and > sharing. > > 17) Cross-lingual, multilingual and multimodal aspects in event analysis. > > 18) Resources and approaches related to contentious politics around > climate change. > > **** Shared tasks **** > > Please check the workshop page and Github repositories of the respective > task for additional details. > > Task 1 - Multilingual protest news detection: > > The performance of an automated system depends on the target event type as > it may be broad or potentially the event trigger(s) can be ambiguous. The > context of the trigger occurrence may need to be handled as well. For > instance, the ‘protest’ event type may be synonymous with ‘demonstration’ > or not in a specific context. Moreover, hypothetical cases such as future > protest plans may need to be excluded from the results. Finally, the > relevance of a protest depends on the actors as in a contentious political > event only citizen-led events are in the scope. This challenge becomes even > harder in a cross-lingual and zero-shot setting in case training data are > not available in new languages. We tackle the task in four steps and hope > state-of-the-art approaches will yield optimal results. > > Contact person: Ali Hürriyetoğlu ([email protected]) > > Github: https://github.com/emerging-welfare/case-2022-multilingual-event > > Task 2 - Collecting and Geocoding Armed Clash Events in Russian Ukrainian > Conflict: > > There is a mismatch between the event information collected between > automated and manual approaches. We aim at identifying similarities and > differences between the results of these paradigms for creating event > datasets. The participants of Task 1 will be invited to run the systems > they will develop to tackle Task 1 on a text archive. Participation in Task > 1 is not a precondition to participate in Task 2. > > Contact person: Hristo Tanev ([email protected]) and Onur Uca ( > [email protected]) > > Github: https://github.com/zavavan/case2023_task2 > > Task 3 - Event causality identification: > > Causality is a core cognitive concept and appears in many natural language > processing (NLP) works that aim to tackle inference and understanding. We > are interested in studying event causality in news, and therefore, > introduce the Causal News Corpus. The Causal News Corpus consists of 3,767 > event sentences, extracted from protest event news, that have been > annotated with sequence labels on whether it contains causal relations or > not. Subsequently, causal sentences are also annotated with Cause, Effect > and Signal spans. Our subtasks work on the Causal News Corpus, and we hope > that accurate, automated solutions may be proposed for the detection and > extraction of causal events in news. > > Contact person: Fiona Anting Tan ([email protected]) > > Github: https://github.com/tanfiona/CausalNewsCorpus > > > Task 4 - Multimodal Hate Speech Event Detection: > > Hate speech detection is one of the most important aspects of event > identification during political events like invasions. In the case of hate > speech detection, the event is the occurrence of hate speech, the entity is > the target of the hate speech, and the relationship is the connection > between the two. Since multimodal content is widely prevalent across the > internet, the detection of hate speech in text-embedded images is very > important. Given a text-embedded image, this task aims to automatically > identify the hate speech and its targets. This task will have two subtasks. > > Contact person: Surendrabikram Thapa ([email protected]) > > Github: https://github.com/therealthapa/case2023_task4 > > > > **** Deadlines for the Shared tasks **** > > ** Task 1, 3, 4: > > Training & Validation data available: May 1, 2023 > > Test data available: Jun 15, 2023 > > Test start: Jun 15, 2023 > > Test end: Jun 30, 2023 > > System Description Paper submissions due: Jul 10, 2023 > > Notification to authors after review: Aug 5, 2023 > > Camera ready: Aug 25, 2023 > > > ** Task 2: > > Sample Text archive is available: May 22, 2023 > > Text archive for evaluation is available: July 1, 2023 > > Evaluation period starts: July 1, 2023 > > Evaluation period ends: July 24, 2023 > > System Description Paper submissions due: July 31, 2023 > > Notification to authors after review: August 7, 2023 > > Camera ready: August 25, 2023 > > > *** Keynotes *** > > We will continue our tradition of inviting keynote speakers from both > social and computational sciences. The social science keynote will be > delivered by Erdem Yörük with the title “Using Automated Text Processing to > Understand Social Movements and Human Behaviour” and the computational ones > will be delivered by Ruslan Mitkov and Kiril Simov. > > > Please see the workshop webpage (https://emw.ku.edu.tr/case-2023/) for > additional details. >
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