Papers by Jonas Kuhn
DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time (2020.lrec-1)
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Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn, Sebastian Padó
| Challenge: | a dataset for germany covering the public debate on immigration is annotated . a political science notion of a claim is used to represent the political discourse . |
| Approach: | They annotate a dataset for german public debate on immigration in 2015 using a political science notion of a claim . they identify claims in newspaper articles, assign them to actors and fine-grained categories and annotize their polarity and date. |
| Outcome: | The dataset is annotated by a political science framework and shows it captures political debate . it shows that political actors can change their positions and take a strong stand against them . |
CCOHA: Clean Corpus of Historical American English (2020.lrec-1)
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| Challenge: | Existing methods to model language change in diachronic studies have been used to overcome its limitations. |
| Approach: | They propose to use the corpus of historical american english to overcome its limitations . they use a downloadable version of the corpora to remove inconsistent lemmas and malformed tokens . |
| Outcome: | The proposed corpus overcomes its main limitations without compromising its qualitative and distributional properties. |
Lexical Semantic Change Discovery (2021.acl-long)
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| Challenge: | Existing approaches to Lexical Semantic Change Detection are limited. |
| Approach: | They propose a shift from change detection to change discovery by fine-tuning a type-based and a token-based approach on recently published German data. |
| Outcome: | The proposed models can be applied to discover new words undergoing meaning change from the full corpus vocabulary. |
Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model (2021.starsem-1)
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| Challenge: | Word Usage Graphs capture fine-grained semantic proximity distinctions between word uses. |
| Approach: | They propose to model word use Graphs using a Bayesian weighted stochastic block model and a probabilistic weightes-based model to capture fine-grained semantic proximity distinctions between word uses. |
| Outcome: | The proposed model is compared with existing models and is empirically most adequate. |
Investigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification (2022.lrec-1)
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| Challenge: | Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate due to domain experts taking a lot of time working through all the classes. |
| Approach: | They propose to build classifiers on multi-label text datasets using Active Learning to reduce labeling effort. |
| Outcome: | The proposed classifiers can be used to reduce labeling effort on multi-label datasets. |
»textklang« – Towards a Multi-Modal Exploration Platform for German Poetry (2022.lrec-1)
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Nadja Schauffler, Toni Bernhart, Andre Blessing, Gunilla Eschenbach, Markus Gärtner, Kerstin Jung, Anna Kinder, Julia Koch, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu, Lorenz Wesemann, Jonas Kuhn
| Challenge: | »textklang« aims to explore the relationship between written text and its potential and actual sonic realisation in lyric poetry . the platform will combine three modalities: the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . musical setting of lyrical poetry. |
| Approach: | They propose to combine a multi-modal corpus of German lyric poetry from the Romantic era with a platform for systematic exploration. |
| Outcome: | The platform will combine the poetic text, the audio signal of a recorded recitation and, at a later stage, music scores of . a musical setting of lyric poetry. |
Improving Neural Political Statement Classification with Class Hierarchical Information (2022.findings-acl)
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Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Pado
| Challenge: | skewed classification of fine-grained categories in text-based computational social science is challenging on the NLP side. |
| Approach: | They propose to use hierarchical relations among categories in codebooks to create constraints on the learned model. |
| Outcome: | The proposed model improves on two datasets and multiple languages. |
GRAIN-S: Manually Annotated Syntax for German Interviews (2020.lrec-1)
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| Challenge: | GRAIN-S is a set of manually created syntactic annotations for radio interviews in germany. |
| Approach: | They propose to use GRAIN-S to create syntactic annotations for radio interviews in germany. |
| Outcome: | The proposed dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. |
Evaluating Pre-Trained Sentence-BERT with Class Embeddings in Active Learning for Multi-Label Text Classification (2022.aacl-short)
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| Challenge: | Existing Active Learning strategies for pre-trained transformer language models are limited and do not work well on domain-specific datasets. |
| Approach: | They employ pre-trained transformer sentence embeddings to group samples with the same labels in the embeddable space on a legal document corpus. |
| Outcome: | The proposed method performs significantly worse than baselines on two domain-specific datasets. |
A Lightweight Modeling Middleware for Corpus Processing (L18-1)
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| Challenge: | Present-day empirical research in computational or theoretical linguistics has richly annotated and diverse corpus resources. |
| Approach: | They propose a framework for modeling arbitrary multi-modal corpus resources in a unified form for processing tools. |
| Outcome: | The proposed framework allows researchers to explore and query more diverse corpus resources and artifacts through a single interactive interface. |
Who Sides with Whom? Towards Computational Construction of Discourse Networks for Political Debates (P19-1)
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| Challenge: | a vision of computational construction of discourse networks from newspaper reports is essential for understanding democratic political decision making. |
| Approach: | They propose to use a requirements analysis and an annotated pilot corpus of migration claims to build a computationally-based model of political debates from newspaper reports. |
| Outcome: | The proposed framework could be scaled up to a large scale and be useful for political scientists. |
Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification (2023.findings-acl)
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| Challenge: | Modern text classification systems achieve excellent accuracy across tasks and corpora. |
| Approach: | They propose a Reinforcement Learning policy that uses many different aspects of the data and task to select the most informative unlabeled subset dynamically over the course of the AL procedure. |
| Outcome: | The proposed framework outperforms baselines on four complex multi-class, multi-label text classification datasets. |
Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)
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| Challenge: | Existing methods for decoding dependency trees are 10 times faster than current ones. |
| Approach: | They propose a graph-based method to tackle a dependency tree linearization task . they propose to solve a Traveling Salesman Problem and combine the solution into a projective tree . |
| Outcome: | The proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding. |
Moving TIGER beyond Sentence-Level (L18-1)
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| Challenge: | TIGER 2.2-doc is a new set of annotations for the German TIger corpus. |
| Approach: | They propose a new set of annotations for the German TIGER corpus . they introduce new document-level annotations: authors and their gender. |
| Outcome: | The new annotations improve the TIGER corpus and its structure and authors and gender. |
An Environment for Relational Annotation of Political Debates (P19-3)
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| Challenge: | Scalable text analysis techniques can open corpora to new questions in computational social sciences and digital humanities. |
| Approach: | They describe a tool that allows annotating newspaper text with rich information about claims (demands) raised by politicians and other actors. |
| Outcome: | The MARDY tool realizes the complete workflow necessary for annotating a large newspaper text collection with rich information about claims (demands) raised by politicians and other actors. |
Polyglot Semantic Parsing in APIs (N18-1)
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| Challenge: | Existing approaches to semantic parsing work by training individual models for each available parallel dataset of text-meaning pairs. |
| Approach: | They propose a polyglot semantic translation approach that trains on multiple datasets and natural languages to learn parsing models. |
| Outcome: | The proposed model can be used for parsing a wide variety of natural languages and output languages, and achieves state-of-the-art performance on the above datasets. |
German Radio Interviews: The GRAIN Release of the SFB732 Silver Standard Collection (L18-1)
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Katrin Schweitzer, Kerstin Eckart, Markus Gärtner, Agnieszka Falenska, Arndt Riester, Ina Rösiger, Antje Schweitzer, Sabrina Stehwien, Jonas Kuhn
| Challenge: | GRAIN contains German radio interviews and is annotated on multiple linguistic layers. |
| Approach: | They present GRAIN as part of the SFB732 Silver Standard Collection . GRAIN contains German radio interviews and is annotated on multiple linguistic layers . |
| Outcome: | The GRAIN data set contains German radio interviews and is annotated on multiple linguistic layers. |
The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (P19-1)
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| Challenge: | Existing non-neural dependency parsers benefit from information coming from structural features . however, their successors lack explicit information about the structural context . |
| Approach: | They propose to model the structural context of a biLSTM-based dependency parser with features built from partial subtrees. |
| Outcome: | The proposed models do not use any conventional structural features but capture implicitly the structural context. |
Explaining and Improving BERT Performance on Lexical Semantic Change Detection (2021.eacl-srw)
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| Challenge: | Lexical semantic change detection is still a challenging field due to the success of type-based embeddings in SemEval-2020 Task 1 and other NLP tasks. |
| Approach: | They compare the performance of BERT embeddings with results from the word sense disambiguation dataset underlying SemEval-2020 Task 1 and the Italian follow-up task DIACR-Ita. |
| Outcome: | The proposed model outperforms token-based embeddings on lexical semantic change detection tasks. |
IMSurReal: IMS at the Surface Realization Shared Task 2019 (D19-63)
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| Challenge: | a system for shallow and deep completion is presented for the Surface Realization Shared Task 2019 . the system achieves state-of-the-art performance without using external data. |
| Approach: | They propose a surface realization system that takes five steps without external data . they perform detailed error analysis revealing correlation between word order freedom and difficulty . |
| Outcome: | The proposed system achieves state-of-the-art without external data . it achieves highest BLEU scores on tokenized text and human evaluation on four languages . |
Compound or Term Features? Analyzing Salience in Predicting the Difficulty of German Noun Compounds across Domains (2021.starsem-1)
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| Challenge: | Using domain-specific vocabulary, it is important to analyse domain-related characteristics to improve the communication between lay people and experts. |
| Approach: | They focus on the interaction of compound-based lexical features (such as frequency and productivity) and terminology-based features (contrasting domain-specific and general language) across word representations and classifiers. |
| Outcome: | The proposed model shows that the interaction of compound-based lexical features and terminology-based features across word representations and classifiers is important for a broad binary distinction into ‘easy’ vs. ‘difficult’ general-language compound frequency is sufficient, but for . a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features. |
Bridging resolution: Task definition, corpus resources and rule-based experiments (C18-1)
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| Challenge: | Recent work on bridging resolution has been based on the corpus ISNotes, as this was the only corpus available with unrestricted bridling annotations. |
| Approach: | They propose a rule-based system to resolve bridging annotations in ISNotes corpus and apply it to new corpora. |
| Outcome: | The proposed system achieves state-of-the-art performance on ISNotes corpus, but low performance on in-domain corpora. |
The DURel Annotation Tool: Human and Computational Measurement of Semantic Proximity, Sense Clusters and Semantic Change (2024.eacl-demo)
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Dominik Schlechtweg, Shafqat Mumtaz Virk, Pauline Sander, Emma Sköldberg, Lukas Theuer Linke, Tuo Zhang, Nina Tahmasebi, Jonas Kuhn, Sabine Schulte Im Walde
| Challenge: | DURel is an open source tool for semantic proximity between word uses. |
| Approach: | They present an open-source tool for the annotation of semantic proximity between word uses. |
| Outcome: | The proposed tool supports standardized human annotation and computational annotation, building on recent advances with Word-in-Context models. |
Real-Valued Logics for Typological Universals: Framework and Application (2020.coling-main)
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| Challenge: | a framework for typological statements captures the truth value of a formula on a given data source. |
| Approach: | They propose a framework which captures the empirical truth value of a formula on a given data source. |
| Outcome: | The proposed framework can be used to express typological statements on multilingual treebanks with comparable annotation. |
NLATool: an Application for Enhanced Deep Text Understanding (C18-2)
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Markus Gärtner, Sven Mayer, Valentin Schwind, Eric Hämmerle, Emine Turcan, Florin Rheinwald, Gustav Murawski, Lars Lischke, Jonas Kuhn
| Challenge: | a wide range of subfields in natural language processing see systems solving their tasks with sufficiently high-quality levels. |
| Approach: | They propose a web application that supports text annotation and enriches the text with additional information from a number of sources directly within the application. |
| Outcome: | The proposed web application is based on a human-centered design process . it offers a rich visualization of texts and the entities mentioned in them through an easy to use interface. |