LINGUIST List 32.100
Thu Jan 07 2021
Calls: Comp Ling, Gen Ling, Lang Doc, Text/Corpus Ling/Thailand
Editor for this issue: Lauren Perkins <laurenlinguistlist.org>
Ali Hürriyetoğlu <ahurriyetoglu
Challenges and Applications of Automated Extraction of Socio-political Events from Text
ACL-IJCNLP E-mail this message to a friend
Full Title: Challenges and Applications of Automated Extraction of Socio-political Events from Text
Short Title: CASE
Date: 05-Aug-2021 - 06-Aug-2021
Location: Bangkok, Thailand
Contact Person: Ali Hürriyetoğlu
Meeting Email: < click here to access email >
Web Site: https://emw.ku.edu.tr/case-2021/
Linguistic Field(s): Computational Linguistics; General Linguistics; Language Documentation; Text/Corpus Linguistics
Call Deadline: 26-Apr-2021
Today, the unprecedented quantity of easily accessible data on social, political, and economic processes offers ground-breaking potential in guiding data-driven analysis in social and human sciences and in driving informed policy-making processes. The need for precise and high-quality information about a wide variety of events ranging from political violence, environmental catastrophes, and conflict, to international economic and health crises has rapidly escalated (Porta and Diani, 2015; Coleman et al. 2014). Governments, multilateral organizations, local and global NGOs, and social movements present an increasing demand for this data to prevent or resolve conflicts, provide relief for those that are afflicted, or improve the lives of and protect citizens in a variety of ways. For instance, Black Lives Matter protests and conflict in Syria events are only two examples where we must understand, analyze, and improve the real-life situations using such data.
Event extraction has long been a challenge for the natural language processing (NLP) community as it requires sophisticated methods in defining event ontologies, creating language resources, and developing algorithmic approaches (Pustojevsky et al. 2003; Boroş, 2018; Chen et al. 2021). Social and political scientists have been working to create socio-political event databases such as ACLED, EMBERS, GDELT, ICEWS, MMAD, PHOENIX, POLDEM, SPEED, TERRIER, and UCDP following similar steps for decades. These projects and the new ones increasingly rely on machine learning (ML) and NLP methods to deal better with the vast amount and variety of data in this domain (Hürriyetoğlu et al. 2020). Automation offers scholars not only the opportunity to improve existing practices, but also to vastly expand the scope of data that can be collected and studied, thus potentially opening up new research frontiers within the field of socio-political events, such as political violence & social movements. But automated approaches as well suffer from major issues like bias, generalizability, class imbalance, training data limitations, and ethical issues that have the potential to affect the results and their use drastically (Lau and Baldwin 2020; Bhatia et al. 2020; Chang et al. 2019). Moreover, the results of the automated systems for socio-political event information collection may not be comparable to each other or not of sufficient quality (Wang et al. 2016; Schrodt 2020).
Socio-political events are varied and nuanced. Both the political context and the local language used may affect whether and how they are reported. Therefore, all steps of information collection (event definition, language resources, and manual or algorithmic steps) may need to be constantly updated, leading to a series of challenging questions: Do events related to minority groups are represented well? Are new types of events covered? Are the event definitions and their operationalization comparable across systems (Hürriyetoğlu 2019, 2020a, 2020b)? This workshop aims to seek answers to these kind of questions, to inspire innovative technological and scientific solutions for tackling the aforementioned issues, and to quantify the quality of the automated event extraction systems. Moreover, the workshop will trigger a deeper understanding of the performance of the computational tools used and the usability of the resulting socio-political event datasets.
Call for Papers:
We invite contributions from researchers in computer science, NLP, ML, 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. Social and political scientists will be interested in reporting and discussing their approaches and observe what the state-of-the-art text processing systems can achieve for their domain. Computational scholars will have the opportunity to illustrate the capacity of their approaches in this domain and benefit from being challenged by real-world use cases. Academic workshops specific to tackling event information in general or for analyzing text in specific domains such as health, law, finance, and biomedical sciences have significantly accelerated progress in these topics and fields, respectively. However, there is not a comparable effort for handling socio-political events. We hope to fill this gap and contribute to social and political sciences in a similar spirit. We invite work on all aspects of automated coding of socio-political events from mono- or multi-lingual text sources. This includes (but is not limited to) the following topics:
- Extracting events in and beyond a sentence
- Training data collection and annotation processes
- Event coreference detection
- Event-event relations, e.g., subevents, main events, 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, and pragmatic aspects of event information manifestation
- Development and analysis of rule-based, ML, hybrid, and human-in-the-loop approaches for creating event datasets
- COVID-19 related socio-political events
- Applications of event databases
- Online social movements
- Bias and fairness of the sources and event datasets
- Estimating what is missing in event datasets using internal and external information
- Novel event detection
- Release of new event datasets
- Ethics, misinformation, privacy, and fairness concerns pertaining to event datasets
- Copyright issues on event dataset creation, dissemination, and sharing
- Qualities of the event information on various online and offline platforms
This call solicits full papers reporting original and unpublished research on the topics listed above. The papers should emphasize obtained results rather than intended work and should indicate clearly the state of completion of the reported results. Submissions should be between 4 and 8 pages in total.
Authors are also invited to submit short papers not exceeding 4 pages (plus two additional pages for references). Short papers should describe:
- a small, focused contribution;
- work in progress;
- a negative result;
- a position paper.
- a report on shared task participation.
Papers should be submitted on the START page of the workshop (link:TBD) in PDF format, in compliance with the ACL 2021 author guidelines provided on https://2021.aclweb.org/calls/papers
The reviewing process will be double blind and papers should not include the authors’ names and affiliations. Each submission will be reviewed by at least three members of the program committee. If you do include any author names on the title page, your submission will be automatically rejected. In the body of your submission, you should eliminate all direct references to your own previous work.
Workshop Proceedings will be published on ACL Anthology.
Page Updated: 07-Jan-2021