Call for Workshop Papers and Shared Task Participation: Automated Extraction of Socio-political Events from Text - CASE @ RANLP 2023
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
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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/
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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 (ali.hurriyetoglu@gmail.com)
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 (htanev@gmail.com) and Onur Uca ( onuruca@mersin.edu.tr)
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 (tan.f@u.nus.edu)
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 (surendrabikram@vt.edu)
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.
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
- Extracting events and their arguments such as time and location in and
beyond a sentence or document, event coreference resolution.
- 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.
- New datasets, training data collection, and annotation for event
information.
- Event-event relations, e.g., subevents, main events, spatio-temporal
relations, causal relations.
Event dataset evaluation in light of reliability and validity metrics.
Defining, populating, and facilitating event schemas and ontologies.
Automated tools and pipelines for event collection related tasks.
Lexical, syntactic, semantic, discursive, and pragmatic aspects of
event manifestation.
- Methodologies for development, evaluation, and analysis of event
datasets.
- Applications of event databases, e.g. early warning, conflict
prediction, policymaking.
- Estimating what is missing in event datasets using internal and
external information.
Detection of new and emerging SPE types, e.g. creative protests.
Release of new event datasets.
Bias and fairness of the sources and event datasets.
Ethics, misinformation, privacy, and fairness concerns pertaining to
event datasets.
- Copyright issues on event dataset creation, dissemination, and
sharing.
Cross-lingual, multilingual and multimodal aspects in event analysis.
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 (ali.hurriyetoglu@gmail.com)
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 (htanev@gmail.com) and Onur Uca ( onuruca@mersin.edu.tr)
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 (tan.f@u.nus.edu)
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 (surendrabikram@vt.edu)
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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ali hürriyetoglu