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Found 16 papers, 2 papers with code
PROTEST-ER: Retraining BERT for Protest Event Extraction
We analyze the effect of further retraining BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction.
Multilingual Protest News Detection - Shared Task 1, CASE 2021
Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (subtask 3), and event extraction (subtask 4).
Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models.
Federated learning in food research
Research in the food domain is at times limited due to data sharing obstacles, such as data ownership, privacy requirements, and regulations.
GLOCON Database: Design Decisions and User Manual (v1.0)
GLOCON is a database of contentious events automatically extracted from national news sources from various countries in multiple languages.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report
We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023.
Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022
The CASE 2022 extension consists of expanding the test data with more data in previously available languages, namely, English, Hindi, Portuguese, and Spanish, and adding new test data in Mandarin, Turkish, and Urdu for Sub-task 1, document classification.
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report
We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022.
Global Contentious Politics Database (GLOCON) Annotation Manuals
In order to assure these, the annotation manuals in this document lay out the rules according to which annotators code the news articles.
Utilizing coarse-grained data in low-data settings for event extraction
Annotating text data for event information extraction systems is hard, expensive, and error-prone.
Event Coreference Resolution for Contentious Politics Events
We propose a dataset for event coreference resolution, which is based on random samples drawn from multiple sources, languages, and countries.
COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules
In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules.
Cross-context News Corpus for Protest Events related Knowledge Base Construction
For each news source, the annotation starts on random samples of news articles and continues with samples that are drawn using active learning.
Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-context Setting
We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing.
Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report
The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection.
Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT
This paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019).
