Michael Wiegand


2021

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Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis
Michael Wiegand | Maja Geulig | Josef Ruppenhofer
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We examine the task of detecting implicitly abusive comparisons (e.g. “Your hair looks like you have been electrocuted”). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. “dumbass” or “scum”) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.

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Exploiting Emojis for Abusive Language Detection
Michael Wiegand | Josef Ruppenhofer
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We propose to use abusive emojis, such as the “middle finger” or “face vomiting”, as a proxy for learning a lexicon of abusive words. Since it represents extralinguistic information, a single emoji can co-occur with different forms of explicitly abusive utterances. We show that our approach generates a lexicon that offers the same performance in cross-domain classification of abusive microposts as the most advanced lexicon induction method. Such an approach, in contrast, is dependent on manually annotated seed words and expensive lexical resources for bootstrapping (e.g. WordNet). We demonstrate that the same emojis can also be effectively used in languages other than English. Finally, we also show that emojis can be exploited for classifying mentions of ambiguous words, such as “fuck” and “bitch”, into generally abusive and just profane usages.

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Implicitly Abusive Language – What does it actually look like and why are we not getting there?
Michael Wiegand | Josef Ruppenhofer | Elisabeth Eder
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Abusive language detection is an emerging field in natural language processing which has received a large amount of attention recently. Still the success of automatic detection is limited. Particularly, the detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words (e.g. dumbass or scum), is not working well. In this position paper, we explain why existing datasets make learning implicit abuse difficult and what needs to be changed in the design of such datasets. Arguing for a divide-and-conquer strategy, we present a list of subtypes of implicitly abusive language and formulate research tasks and questions for future research.

2020

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Doctor Who? Framing Through Names and Titles in German
Esther van den Berg | Katharina Korfhage | Josef Ruppenhofer | Michael Wiegand | Katja Markert
Proceedings of the 12th Language Resources and Evaluation Conference

Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. We investigate entity framing of political figures through the use of names and titles in German online discourse, enhancing current research in entity framing through titling and naming that concentrates on English only. We collect tweets that mention prominent German politicians and annotate them for stance. We find that the formality of naming in these tweets correlates positively with their stance. This confirms sociolinguistic observations that naming and titling can have a status-indicating function and suggests that this function is dominant in German tweets mentioning political figures. We also find that this status-indicating function is much weaker in tweets from users that are politically left-leaning than in tweets by right-leaning users. This is in line with observations from moral psychology that left-leaning and right-leaning users assign different importance to maintaining social hierarchies.

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Enhancing a Lexicon of Polarity Shifters through the Supervised Classification of Shifting Directions
Marc Schulder | Michael Wiegand | Josef Ruppenhofer
Proceedings of the 12th Language Resources and Evaluation Conference

The sentiment polarity of an expression (whether it is perceived as positive, negative or neutral) can be influenced by a number of phenomena, foremost among them negation. Apart from closed-class negation words like “no”, “not” or “without”, negation can also be caused by so-called polarity shifters. These are content words, such as verbs, nouns or adjectives, that shift polarities in their opposite direction, e.g. “abandoned” in “abandoned hope” or “alleviate” in “alleviate pain”. Many polarity shifters can affect both positive and negative polar expressions, shifting them towards the opposing polarity. However, other shifters are restricted to a single shifting direction. “Recoup” shifts negative to positive in “recoup your losses”, but does not affect the positive polarity of “fortune” in “recoup a fortune”. Existing polarity shifter lexica only specify whether a word can, in general, cause shifting, but they do not specify when this is limited to one shifting direction. To address this issue we introduce a supervised classifier that determines the shifting direction of shifters. This classifier uses both resource-driven features, such as WordNet relations, and data-driven features like in-context polarity conflicts. Using this classifier we enhance the largest available polarity shifter lexicon.

2019

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Detection of Abusive Language: the Problem of Biased Datasets
Michael Wiegand | Josef Ruppenhofer | Thomas Kleinbauer
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We discuss the impact of data bias on abusive language detection. We show that classification scores on popular datasets reported in previous work are much lower under realistic settings in which this bias is reduced. Such biases are most notably observed on datasets that are created by focused sampling instead of random sampling. Datasets with a higher proportion of implicit abuse are more affected than datasets with a lower proportion.

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Detecting Derogatory Compounds – An Unsupervised Approach
Michael Wiegand | Maximilian Wolf | Josef Ruppenhofer
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We examine the new task of detecting derogatory compounds (e.g. “curry muncher”). Derogatory compounds are much more difficult to detect than derogatory unigrams (e.g. “idiot”) since they are more sparsely represented in lexical resources previously found effective for this task (e.g. Wiktionary). We propose an unsupervised classification approach that incorporates linguistic properties of compounds. It mostly depends on a simple distributional representation. We compare our approach against previously established methods proposed for extracting derogatory unigrams.

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Not My President: How Names and Titles Frame Political Figures
Esther van den Berg | Katharina Korfhage | Josef Ruppenhofer | Michael Wiegand | Katja Markert
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

Naming and titling have been discussed in sociolinguistics as markers of status or solidarity. However, these functions have not been studied on a larger scale or for social media data. We collect a corpus of tweets mentioning presidents of six G20 countries by various naming forms. We show that naming variation relates to stance towards the president in a way that is suggestive of a framing effect mediated by respectfulness. This confirms sociolinguistic theory of naming and titling as markers of status.

2018

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Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German
Marc Schulder | Michael Wiegand | Josef Ruppenhofer
Proceedings of the 27th International Conference on Computational Linguistics

In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.

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Distinguishing affixoid formations from compounds
Josef Ruppenhofer | Michael Wiegand | Rebecca Wilm | Katja Markert
Proceedings of the 27th International Conference on Computational Linguistics

We study German affixoids, a type of morpheme in between affixes and free stems. Several properties have been associated with them – increased productivity; a bleached semantics, which is often evaluative and/or intensifying and thus of relevance to sentiment analysis; and the existence of a free morpheme counterpart – but not been validated empirically. In experiments on a new data set that we make available, we put these key assumptions from the morphological literature to the test and show that despite the fact that affixoids generate many low-frequency formations, we can classify these as affixoid or non-affixoid instances with a best F1-score of 74%.

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Inducing a Lexicon of Abusive Words – a Feature-Based Approach
Michael Wiegand | Josef Ruppenhofer | Anna Schmidt | Clayton Greenberg
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We address the detection of abusive words. The task is to identify such words among a set of negative polar expressions. We propose novel features employing information from both corpora and lexical resources. These features are calibrated on a small manually annotated base lexicon which we use to produce a large lexicon. We show that the word-level information we learn cannot be equally derived from a large dataset of annotated microposts. We demonstrate the effectiveness of our (domain-independent) lexicon in the cross-domain detection of abusive microposts.

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Disambiguation of Verbal Shifters
Michael Wiegand | Sylvette Loda | Josef Ruppenhofer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Introducing a Lexicon of Verbal Polarity Shifters for English
Marc Schulder | Michael Wiegand | Josef Ruppenhofer | Stephanie Köser
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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A Survey on Hate Speech Detection using Natural Language Processing
Anna Schmidt | Michael Wiegand
Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.

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Evaluating the morphological compositionality of polarity
Josef Ruppenhofer | Petra Steiner | Michael Wiegand
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.

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Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features
Marc Schulder | Michael Wiegand | Josef Ruppenhofer | Benjamin Roth
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a major step towards the creation of the first high-coverage lexicon of polarity shifters. In this work, we bootstrap a lexicon of verbs by exploiting various linguistic features. Polarity shifters, such as “abandon”, are similar to negations (e.g. “not”) in that they move the polarity of a phrase towards its inverse, as in “abandon all hope”. While there exist lists of negation words, creating comprehensive lists of polarity shifters is far more challenging due to their sheer number. On a sample of manually annotated verbs we examine a variety of linguistic features for this task. Then we build a supervised classifier to increase coverage. We show that this approach drastically reduces the annotation effort while ensuring a high-precision lexicon. We also show that our acquired knowledge of verbal polarity shifters improves phrase-level sentiment analysis.

2016

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Separating Actor-View from Speaker-View Opinion Expressions using Linguistic Features
Michael Wiegand | Marc Schulder | Josef Ruppenhofer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Opinion Holder and Target Extraction on Opinion Compounds – A Linguistic Approach
Michael Wiegand | Christine Bocionek | Josef Ruppenhofer
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Opinion Holder and Target Extraction for Verb-based Opinion Predicates – The Problem is Not Solved
Michael Wiegand | Marc Schulder | Josef Ruppenhofer
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Ordering adverbs by their scaling effect on adjective intensity
Josef Ruppenhofer | Jasper Brandes | Petra Steiner | Michael Wiegand
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Opinion Holder and Target Extraction based on the Induction of Verbal Categories
Michael Wiegand | Josef Ruppenhofer
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

2014

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Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
Michael Wiegand | Benjamin Roth | Dietrich Klakow
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Comparing methods for deriving intensity scores for adjectives
Josef Ruppenhofer | Michael Wiegand | Jasper Brandes
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Separating Brands from Types: an Investigation of Different Features for the Food Domain
Michael Wiegand | Dietrich Klakow
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Towards Contextual Healthiness Classification of Food Items - A Linguistic Approach
Michael Wiegand | Dietrich Klakow
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Predicative Adjectives: An Unsupervised Criterion to Extract Subjective Adjectives
Michael Wiegand | Josef Ruppenhofer | Dietrich Klakow
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Towards the Detection of Reliable Food-Health Relationships
Michael Wiegand | Dietrich Klakow
Proceedings of the Workshop on Language Analysis in Social Media

2012

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MLSA — A Multi-layered Reference Corpus for German Sentiment Analysis
Simon Clematide | Stefan Gindl | Manfred Klenner | Stefanos Petrakis | Robert Remus | Josef Ruppenhofer | Ulli Waltinger | Michael Wiegand
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper, we describe MLSA, a publicly available multi-layered reference corpus for German-language sentiment analysis. The construction of the corpus is based on the manual annotation of 270 German-language sentences considering three different layers of granularity. The sentence-layer annotation, as the most coarse-grained annotation, focuses on aspects of objectivity, subjectivity and the overall polarity of the respective sentences. Layer 2 is concerned with polarity on the word- and phrase-level, annotating both subjective and factual language. The annotations on Layer 3 focus on the expression-level, denoting frames of private states such as objective and direct speech events. These three layers and their respective annotations are intended to be fully independent of each other. At the same time, exploring for and discovering interactions that may exist between different layers should also be possible. The reliability of the respective annotations was assessed using the average pairwise agreement and Fleiss' multi-rater measures. We believe that MLSA is a beneficial resource for sentiment analysis research, algorithms and applications that focus on the German language.

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A Gold Standard for Relation Extraction in the Food Domain
Michael Wiegand | Benjamin Roth | Eva Lasarcyk | Stephanie Köser | Dietrich Klakow
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.

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Generalization Methods for In-Domain and Cross-Domain Opinion Holder Extraction
Michael Wiegand | Dietrich Klakow
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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The Role of Predicates in Opinion Holder Extraction
Michael Wiegand | Dietrich Klakow
Proceedings of the RANLP 2011 Workshop on Information Extraction and Knowledge Acquisition

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Convolution Kernels for Subjectivity Detection
Michael Wiegand | Dietrich Klakow
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)

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Prototypical Opinion Holders: What We can Learn from Experts and Analysts
Michael Wiegand | Dietrich Klakow
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Convolution Kernels for Opinion Holder Extraction
Michael Wiegand | Dietrich Klakow
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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A survey on the role of negation in sentiment analysis
Michael Wiegand | Alexandra Balahur | Benjamin Roth | Dietrich Klakow | Andrés Montoyo
Proceedings of the Workshop on Negation and Speculation in Natural Language Processing

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Predictive Features for Detecting Indefinite Polar Sentences
Michael Wiegand | Dietrich Klakow
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In recent years, text classification in sentiment analysis has mostly focused on two types of classification, the distinction between objective and subjective text, i.e. subjectivity detection, and the distinction between positive and negative subjective text, i.e. polarity classification. So far, there has been little work examining the distinction between definite polar subjectivity and indefinite polar subjectivity. While the former are utterances which can be categorized as either positive or negative, the latter cannot be categorized as either of these two categories. This paper presents a small set of domain independent features to detect indefinite polar sentences. The features reflect the linguistic structure underlying these types of utterances. We give evidence for the effectiveness of these features by incorporating them into an unsupervised rule-based classifier for sentence-level analysis and compare its performance with supervised machine learning classifiers, i.e. Support Vector Machines (SVMs) and Nearest Neighbor Classifier (kNN). The data used for the experiments are web-reviews collected from three different domains.

2009

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Predictive Features in Semi-Supervised Learning for Polarity Classification and the Role of Adjectives
Michael Wiegand | Dietrich Klakow
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)

2008

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Cost-Sensitive Learning in Answer Extraction
Michael Wiegand | Jochen L. Leidner | Dietrich Klakow
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

One problem of data-driven answer extraction in open-domain factoid question answering is that the class distribution of labeled training data is fairly imbalanced. In an ordinary training set, there are far more incorrect answers than correct answers. The class-imbalance is, thus, inherent to the classification task. It has a deteriorating effect on the performance of classifiers trained by standard machine learning algorithms. They usually have a heavy bias towards the majority class, i.e. the class which occurs most often in the training set. In this paper, we propose a method to tackle class imbalance by applying some form of cost-sensitive learning which is preferable to sampling. We present a simple but effective way of estimating the misclassification costs on the basis of class distribution. This approach offers three benefits. Firstly, it maintains the distribution of the classes of the labeled training data. Secondly, this form of meta-learning can be applied to a wide range of common learning algorithms. Thirdly, this approach can be easily implemented with the help of state-of-the-art machine learning software.