Papers by Michael Wiegand
Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)
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| Challenge: | Existing datasets make learning implicit abuse difficult, argues a new position paper . a lack of work on implicit abuse has limited the effectiveness of automatic detection . |
| Approach: | They argue that existing datasets make learning implicit abuse difficult . they propose a divide-and-conquer strategy to detect implicit abuse . |
| Outcome: | The proposed model could be improved to detect implicit abuse in a dataset with a standardized model. |
The Relevance of Value Systems for Offensive Language Detection (2026.eacl-long)
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| Challenge: | Recent research in perspectivism has departed from the assumption that offensiveness can be defined through a universal perspective. |
| Approach: | They propose to use a dataset consisting of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems to identify offensiveness patterns. |
| Outcome: | The proposed dataset consists of neutrally-phrased sentences on controversial topics, evaluated by individuals from 4 different value systems. |
“Beste Grüße, Maria Meyer” — Pseudonymization of Privacy-Sensitive Information in Emails (2022.lrec-1)
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| Challenge: | exploding amount of user-generated content has spurred research to deal with documents from various digital communication formats. |
| Approach: | They propose to identify text spans that carry information revealing an individual’s identity and substitute them with synthetically generated surrogates. |
| Outcome: | The proposed model is based on a German-language email corpus and evaluates its training data on pseudonymized data. |
Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025.coling-main)
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| Challenge: | Current research focuses on explicit abusive language, but subtler forms of IAL remain insufficiently studied. |
| Approach: | They evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. |
| Outcome: | The proposed models outperform the best previously reported methods in classifying sentences directly as IAL or benign and extracting linguistic features associated with IAL. |
Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis (2021.eacl-main)
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| Challenge: | Using crowdsourcing, we can detect implicitly abusive comparisons . Abusive language is defined as hurtful, derogatory or obscene utterances made by one person to another . |
| Approach: | They propose to use crowdsourcing to generate a dataset for detecting implicitly abusive comparisons . they also use a range of linguistic features to better understand abusive comparison mechanisms . |
| Outcome: | The proposed dataset includes measures to obtain representative and unbiased comparisons. |
Distinguishing affixoid formations from compounds (C18-1)
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| Challenge: | affixoids are morphemes in between affids and free stems that have been associated with increased productivity and a bleached semantics but not empirically validated. |
| Approach: | They propose to use affixoids as morphemes in between affids and stems to test their classification in a subset of German words that includes many hapaxes. |
| Outcome: | The proposed morpheme can be classed as affixoid or non-affixoids with a best F1 score of 74% on a subset of German words that includes many hapaxes . |
Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)
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| Challenge: | Currently, only explicit abuse can be reliably detected due to the increasing amount of abusive language on the Web. |
| Approach: | They propose a crowdsourced dataset that can detect euphemistic abuse by paraphrasing simple explicit utterances. |
| Outcome: | The proposed classifier augments training data with automatically-generated GPT-3 completions. |
Inducing a Lexicon of Abusive Words – a Feature-Based Approach (N18-1)
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| Challenge: | a new classification task is needed to identify abusive words among a set of negative polar expressions. |
| Approach: | They propose to calibrate a domain-independent lexicon for detection of abusive words . they use a small manually annotated base lexico to calibrated a large lexical . |
| Outcome: | The proposed feature can be calibrated on a small manually annotated base lexicon and produced on large datasets. |
Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German (C18-1)
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| Challenge: | a large number of verbal polarity shifters are available for multiple languages, but only English has a sizable lexicon of them. |
| Approach: | They use methods to create large lexicon of verbal polarity shifters in germany . they bootstrap annotated verbs with a supervised classifier and apply them to German . |
| Outcome: | The proposed method is able to create a large lexicon of verbal polarity shifters in germany . it reduces annotation effort by leveraging cross-lingual information from the English lexico . |
Disambiguation of Verbal Shifters (L18-1)
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| Challenge: | Negation is a contextual phenomenon that needs to be addressed in sentiment analysis. |
| Approach: | They propose a supervised learning approach to disambiguate verbal shifters using generalization features and a new lexicon. |
| Outcome: | The proposed approach takes into account various features, particularly generalization features. |
Introducing a Lexicon of Verbal Polarity Shifters for English (L18-1)
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| Challenge: | Negation words can change the sentiment polarity of a phrase, but there are more than 1200 other polarities. |
| Approach: | They propose a lexicon of verbal polarity shifters that covers the entirety of verbs found in WordNet. |
| Outcome: | The proposed lexicon covers the entirety of verbs found in WordNet. |
Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach (2022.naacl-main)
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| Challenge: | Existing datasets displaying high degree of implicit abuse are biased . current methods focus on explicit abuse, but there is little work on implicit forms of abuse . |
| Approach: | They propose to model atomic negative sentences to address implicit abuse by addressing its different subtypes and then separate them into subtype. |
| Outcome: | The proposed approach generalizes across different identities and languages. |
Beyond Negative Stereotypes – Non-Negative Abusive Utterances about Identity Groups and Their Semantic Variants (2025.acl-long)
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| Challenge: | implicitly abusive language is a language that could offend, demean or marginalize another person . a large portion of what is considered abusive language can be classified as implicitly abused . |
| Approach: | They propose to profile implicitly abusive language and use it to analyze a dataset of such utterances. |
| Outcome: | The proposed dataset identifies the type of abusive language that is not conveyed by unambiguously abusive words. |
A Question of Style: A Dataset for Analyzing Formality on Different Levels (2023.findings-eacl)
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| Challenge: | Using machine learning, we can produce contextually appropriate language. |
| Approach: | They present a dataset of German sentence-level formality assessed on a continuous informal-formal scale. |
| Outcome: | The proposed dataset compares sentences from a wide range of genres assessed on a continuous informal-formal scale. |
Oddballs and Misfits: Detecting Implicit Abuse in Which Identity Groups are Depicted as Deviating from the Norm (2024.emnlp-main)
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| Challenge: | Abusive language is often defined as hurtful, derogatory or obscene utterances made by one person to another. |
| Approach: | They propose to use a dataset to detect abusive sentences in identity groups . they also report on classification experiments. |
| Outcome: | The proposed dataset includes 7 identity groups and includes classification experiments. |
Exploiting Emojis for Abusive Language Detection (2021.eacl-main)
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| Challenge: | emojis can be used as a proxy for learning a lexicon of abusive words . eliot safina and samuel khan are the authors of this paper . |
| Approach: | They propose to use abusive emojis as a proxy for learning a lexicon of abusive words. |
| Outcome: | The proposed approach generates a lexicon that performs as well as the most advanced lexical induction method. |
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments (2022.coling-1)
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| Challenge: | Unlike previous work, we do not restrict biographical relevance to a small fixed set of pre-defined relations. |
| Approach: | They propose a dataset comprising tweets for the novel task of detecting biographically relevant utterances. |
| Outcome: | The proposed dataset focuses on biographical information on ordinary users of Twitter. |
Detecting Derogatory Compounds – An Unsupervised Approach (N19-1)
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| Challenge: | Derogatory compounds are more difficult to detect than derogatory unigrams since they are sparsely represented in general-purpose lexical resources. |
| Approach: | They propose an unsupervised classification approach that incorporates linguistic properties of compounds. |
| Outcome: | The proposed method is compared with existing methods for extracting derogatory unigrams . the proposed method uses a distributional representation to incorporate linguistic properties of compounds . |
Doctor Who? Framing Through Names and Titles in German (2020.lrec-1)
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| Challenge: | Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. |
| Approach: | They investigate entity framing of political figures through the use of names and titles in German online discourse. |
| Outcome: | The proposed method improves existing studies on German political discourse . it shows that the formality of naming correlates positively with stance in the tweets . |
Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions (2020.lrec-1)
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| Challenge: | Existing polarity shifter lexica only specify when a word can cause shifting, but do not specify when this is limited to a single shifting direction. |
| Approach: | They propose a classifier that determines the shifting direction of polarity shifters by using resource-driven features and data-driven feature. |
| Outcome: | The proposed classifier enhances the largest available polarity shifter lexicon. |
Detection of Abusive Language: the Problem of Biased Datasets (N19-1)
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| Challenge: | Recent studies have reported high classification performance on datasets with difficult cases of abusive language. |
| Approach: | They examine the impact of data bias on abusive language detection by focusing on specific microposts rather than random sampling. |
| Outcome: | The proposed method is more accurate and more accurate than random sampling. |