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Tina Lommel

dblp:411/4515 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 50% Trustworthy machine learning · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis
abusive language detection
0.912025
Beyond Negative Stereotypes - Non-Negative Abusive Utterances about Identity Groups and Their Semantic Variants · ACL (1) 2025
Machine learning › Trustworthy machine learning
fairness
0.912025
Beyond Negative Stereotypes - Non-Negative Abusive Utterances about Identity Groups and Their Semantic Variants · ACL (1) 2025

Methods — techniques the papers use, named apart from their topics

classification · 0.9
YearPublicationVenuePosition
2025 Beyond Negative Stereotypes - Non-Negative Abusive Utterances about Identity Groups and Their Semantic Variants
abstract
We investigate a specific subtype of implicitly abusive language, focusing on non-negative sentences about identity groups (e.g.Women make good cooks).We introduce a novel dataset comprising such utterances.It not only profiles abusive sentences but also includes various semantic variants of the same characteristic attributed to an identity group, allowing us to systematically examine the impact of different degrees of generalization and perspective framing.Thus we demonstrate that specific variants significantly intensify the perception of abusiveness.By switching identity groups, we highlight how the characteristics often described in stereotypes are not inherently abusive.We also report on classification experiments.
Tina Lommel, Elisabeth Eder, Josef Ruppenhofer, Michael Wiegand
ACL (1)1