Papers by Ines Rehbein

14 papers
Our kind of people? Detecting populist references in political debates (2023.findings-eacl)

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Challenge: Existing literature on populism has only limited agreement on its exact properties .
Approach: They propose a cross-lingual dataset to identify populist rhetoric in text . they propose 'hierarchical' annotation procedure to annotate populist references .
Outcome: The proposed dataset can be used to investigate how political actors talk about The Elite and The People and to study how populist rhetoric is used as a strategic device.
Parsers Know Best: German PP Attachment Revisited (2020.coling-main)

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Challenge: PP attachment is one of the major sources of parser errors and is still one of hardest problems for syntactic parsing.
Approach: They present a realistic evaluation of the potential of different PP attachment systems using fully predicted information as system input.
Outcome: The proposed approach is superior to modelling PP attachment disambiguation as a separate task.
Out of the Mouths of MPs: Speaker Attribution in Parliamentary Debates (2024.lrec-main)

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Challenge: Identifying who says what to whom is an essential prerequisite for analysing human communication.
Approach: They propose a new corpus for speaker attribution in german parliamentary debates . the data includes more than 7,700 manually annotated events of speech, thought and writing . they then apply their model to predict speech events in 20 years of debates and investigate the use of factives in the rhetoric of MPs.
Outcome: The proposed model predicts speech events in 20 years of debates and investigates the use of factives in the rhetoric of MPs.
Who’s in, who’s out? Predicting the Inclusiveness or Exclusiveness of Personal Pronouns in Parliamentary Debates (2022.lrec-1)

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Challenge: clusivity properties of personal pronouns are captured in context, including/excluding audience and/or non-speech act participants.
Approach: They propose a compositional annotation scheme to capture the clusivity properties of personal pronouns in context, which is their ability to construct and manage in-groups and out-group.
Outcome: The proposed schema achieves high inter-annotator agreement with a Cohen’s in the range of 89.7-93.2 and a percentage agreement of > 96%.
How to Do Politics with Words: Investigating Speech Acts in Parliamentary Debates (2024.lrec-main)

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Challenge: a new perspective on framing through the lens of speech acts investigates how politicians make use of different pragmatic speech act functions in political debates.
Approach: They propose a new framework for framing through the lens of speech acts and an annotation scheme for political debates.
Outcome: The proposed framework can predict speech acts with an avg. F1 of around 82.0% . the proposed framework is based on a dataset of German parliamentary debates .
Come hither or go away? Recognising pre-electoral coalition signals in the news (2021.emnlp-main)

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Challenge: In this paper, we decompose the task of recognizing from the news coverage leading up to an election the (un)willingness of political parties to form a coalition into two related, but distinct tasks.
Approach: They propose a task of recognizing from news coverage the (un)willingness of political parties to form a coalition from text and a sub-task of predicting the polarity of the signal.
Outcome: The proposed approach improves over a strong monolingual transfer learning baseline.
Sprucing up the trees – Error detection in treebanks (C18-1)

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Challenge: a method for detecting annotation errors in manually annotated dependency trees is presented . the method is based on ensemble parsing and Bayesian inference guided by active learning .
Approach: They propose a method for detecting annotation errors in manually annotated dependency parse trees . they use ensemble parsing in combination with Bayesian inference guided by active learning .
Outcome: The proposed method detects errors in annotated dependency treebanks and improves parsing accuracy on in- and out-of-domain data.
Neural Reranking for Dependency Parsing: An Evaluation (2020.acl-main)

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Challenge: Recent work shows that neural rerankers can improve dependency parsing results over the top k trees produced by a base parser.
Approach: They propose to use a discriminative reranker to improve dependency parsing results . they propose to incorporate global information into the model to improve parse accuracies .
Outcome: The proposed model outperforms existing models on English and German and Czech, and is the only one to improve on German and Chinese data.
Fine-grained Named Entity Annotations for German Biographic Interviews (2020.lrec-1)

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Challenge: a NER annotation scheme is adapted for a corpus of transcripts of biographic interviews with emigrants to German . a dataset of spoken data and teaser tweets from newspaper sites are used to test the NER inventory.
Approach: They propose a fine-grained NER annotation scheme with 30 labels and apply it to German data.
Outcome: The proposed NER annotations can be applied to spoken data and teaser tweets from newspaper sites and achieve good inter-annotator agreement.
A New Resource for German Causal Language (2020.lrec-1)

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Challenge: Annotations of causal language are challenging for automatic and human annotators.
Approach: They propose a German causal annotation resource with annotations in context for verbs, nouns and prepositions.
Outcome: The proposed annotation scheme distinguishes three types of causal events . the proposed framework also provides annotations for semantic roles and actors .
Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies (2020.lrec-1)

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Challenge: Despite the increasing number of contributions on Part-of-Speech tagging and parsing, automatic processing of user-generated content (UGC) still represents a challenging task.
Approach: They propose a set of guidelines for the annotation of user-generated texts within the Universal Dependencies framework.
Outcome: The proposed annotation guidelines promote cross-linguistic consistency, which has always been in the spirit of UD.
Moral Framing in Politics (MFiP): A new resource and models for moral framing (2025.emnlp-main)

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Challenge: Recent studies have focused on detecting moral values in political communication, trying to identify moral frames used by political actors or parties to convey their messages.
Approach: They propose to code German parliamentary debates to identify moral framing and to detect subtle differences in politicians’ moral framming.
Outcome: The proposed model distinguishes between different types of moral frames and includes narrative roles, together with the moral foundations for each frame.
Improving Sentence Boundary Detection for Spoken Language Transcripts (2020.lrec-1)

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Challenge: Using data expansion and transfer learning, we find that data expansion does not always improve results.
Approach: They propose to divide spoken language into sentence-like units using Topological Fields model . they also propose to use data from the same domain to test different ML architectures .
Outcome: The proposed model improves the detection of boundary detection in spoken dialogues compared to a sequence tagging approach.
A Survey on Modelling Morality for Text Analysis (2024.findings-acl)

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Challenge: Recent work on modelling morality in text has garnered increasing attention due to its complexity and complexity.
Approach: They provide a systematic review of recent work on modelling morality in text . they discuss challenges and research gaps in the area of NLP .
Outcome: The authors present their work on the modelling of morality in text, which has garnered increasing attention in recent years.

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