Papers by Robert Geislinger

5 papers
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)

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Challenge: Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages.
Approach: They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets.
Outcome: The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers.
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)

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Challenge: polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks .
Approach: They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events.
Outcome: The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context.
LECTURE4ALL: A Lightweight Approach to Precise Timestamp Detection in Online Lecture Videos (2025.acl-demo)

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Challenge: Lecture2Go provides a vast collection of recorded lectures, but locating specific content within videos can be time-consuming.
Approach: They present an open-source web application to improve the search experience of educational video platforms.
Outcome: The proposed solution improves the search experience of educational video platforms.
HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation (2025.findings-acl)

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Challenge: Existing approaches to manage hate speech rely on reactive measures such as blocking or suspending offensive messages . despite regulations imposed by nations and social media platforms, hateful content remains a challenge .
Approach: They propose a framework for automated hate speech moderation based on different strategies . they examine hate speech regulations and strategies from three perspectives .
Outcome: The proposed framework could be based on a combination of country regulations, social platform policies, and NLP research datasets.
Concept Over Time Analysis: Unveiling Temporal Patterns for Qualitative Data Analysis (2024.naacl-demo)

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Challenge: Concept Over Time Analysis is a machine-learning-based feature that allows users to define, refine, and visualize concepts of interest within an interactive interface.
Approach: They propose to extend the Discourse Analysis Tool Suite with Concept Over Time Analysis extension that allows users to define, refine, and visualize their concepts of interest within an interactive interface.
Outcome: The proposed system allows users to define, refine, and visualize their concepts of interest within an interactive interface.

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