GenAI4ELab/papercli-papers huggingface dataset:
Rethinking Scene Segmentation. Advancing Automated Detection of Scene Changes in Literary Texts
Authors: Svenja Guhr; Huijun Mao; Fengyi Lin
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Automated scene segmentation is an ongoing challenge in computational literary studies (CLS) to approach literary texts by analyzing comparable units. In this paper, we present our approach (work in progress) to text segmentation using a classifier that identifies the position of a scene change in English-language fiction. By manually annotating novels from a 20th-century US-English romance fiction corpus, we prepared training data for fine-tuning transformer models, yielding promising preliminary results for improving automated text segmentation in CLS.
Sentence-Alignment in Semi-parallel Datasets
Authors: Steffen Frenzel; Manfred Stede
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: In this paper, we are testing sentence alignment on complex, semi-parallel corpora, i.e., different versions of the same text that have been altered to some extent. We evaluate two hypotheses: To make alignment algorithms more efficient, we test the hypothesis that matching pairs can be found in the immediate vicinity of the source sentence and that it is sufficient to search for paraphrases in a ‘context window’. To improve the alignment quality on complex, semi-parallel texts, we test the implementation of a segmentation into Elementary Discourse Units (EDUs) in order to make more precise alignments at this level. Since EDUs are the smallest possible unit for communicating a full proposition, we assume that aligning at this level can improve the overall quality. Both hypotheses are tested and validated with several embedding models on varying degrees of parallel German datasets. The advantages and disadvantages of the different approaches are presented, and our next steps are outlined.
Argumentation in political empowerment on Instagram
Authors: Aenne Knierim; Ulrich Heid
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: This paper adopts a distant reading approach to analyze political empowerment on Instagram. We focus on argument mining and content classification to uncover cooccurences between aspects of political empowerment and argument components. We develop an annotation scheme based on literature in digital political empowerment, classifying content into five primary categories along the aspects of political awareness, personal e-identity and political participation. We implement the modified toulmin scheme for argument component detection. As an example discourse, we chose the German discourses #WirSindMehr and #NieWiederIstJetzt.The upheaval was targeted against right-wing extremism and antisemitism. Political awareness emerged as the dominant category, highlighting convergent public concern against antisemitism and right-wing extremism. Claims and backings often contain statements about societal change and aim to raise consciousness.Calls for participation in offline events appear mostly in non-argumentative texts.
Interpretable Models for Detecting Linguistic Variation in Russian Media: Towards Unveiling Propagandistic Strategies during the Russo-Ukrainian War
Authors: Anastasiia Vestel; Stefania Degaetano-Ortlieb
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: With the start of the full-scale Russian invasion of Ukraine in February 2022, the spread of pro-Kremlin propaganda increased to justify the war, both in the official state media and social media. This position paper explores the theoretical background of propaganda detection in the given context and proposes a thorough methodology to investigate how language has been strategically manipulated to align with ideological goals and adapt to the changing narrative surrounding the invasion. Using the WarMM-2022 corpus, the study seeks to identify linguistic patterns across media types and their evolution over time. By doing so, we aim to enhance the understanding of the role of linguistic strategies in shaping propaganda narratives. The findings are intended to contribute to the broader discussion of information manipulation in politically sensitive contexts.
Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics
Authors: Danqing Chen; Adithi Satish; Rasul Khanbayov; Carolin Schuster; Georg Groh
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: The application of text mining methods is becoming increasingly prevalent, particularly within Humanities and Computational Social Sciences, as well as in a broader range of disciplines. This paper presents an analysis of gender bias in English song lyrics using topic modeling and bias measurement techniques. Leveraging BERTopic, we cluster a dataset of 537,553 English songs into distinct topics and analyze their temporal evolution. Our results reveal a significant thematic shift in song lyrics over time, transitioning from romantic themes to a heightened focus on the sexualization of women. Additionally, we observe a substantial prevalence of profanity and misogynistic content across various topics, with a particularly high concentration in the largest thematic cluster. To further analyse gender bias across topics and genres in a quantitative way, we employ the Single Category Word Embedding Association Test (SC-WEAT) to calculate bias scores for word embeddings trained on the most prominent topics as well as individual genres. The results indicate a consistent male bias in words associated with intelligence and strength, while appearance and weakness words show a female bias. Further analysis highlights variations in these biases across topics, illustrating the interplay between thematic content and gender stereotypes in song lyrics.
Artificial Relationships in Fiction: A Dataset for Advancing NLP in Literary Domains
Authors: Despina Christou; Grigorios Tsoumakas
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Relation extraction (RE) in fiction presents unique NLP challenges due to implicit, narrative-driven relationships. Unlike factual texts, fiction weaves complex connections, yet existing RE datasets focus on non-fiction. To address this, we introduce Artificial Relationships in Fiction (ARF), a synthetically annotated dataset for literary RE. Built from diverse Project Gutenberg fiction, ARF considers author demographics, publication periods, and themes. We curated an ontology for fiction-specific entities and relations, and using GPT-4o, generated artificial relationships to capture narrative complexity. Our analysis demonstrates its value for finetuning RE models and advancing computational literary studies. By bridging a critical RE gap, ARF enables deeper exploration of fictional relationships, enriching NLP research at the intersection of storytelling and AI-driven literary analysis.
Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration
Authors: Jan Fillies; Adrian Paschke
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Algorithmic hate speech detection faces significant challenges due to the diverse definitions and datasets used in research and practice. Social media platforms, legal frameworks, and institutions each apply distinct yet overlapping definitions, complicating classification efforts. This study addresses these challenges by demonstrating that existing datasets and taxonomies can be integrated into a unified model, enhancing prediction performance and reducing reliance on multiple specialized classifiers. The work introduces a universal taxonomy and a hate speech classifier capable of detecting a wide range of definitions within a single framework. Our approach is validated by combining two widely used but differently annotated datasets, showing improved classification performance on an independent test set. This work highlights the potential of dataset and taxonomy integration in advancing hate speech detection, increasing efficiency, and ensuring broader applicability across contexts.
Classifying Textual Genre in Historical Magazines (1875-1990)
Authors: Vera Danilova; Ylva Söderfeldt
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Historical magazines are a valuable resource for understanding the past, offering insights into everyday life, culture, and evolving social attitudes. They often feature diverse layouts and genres. Short stories, guides, announcements, and promotions can all appear side by side on the same page. Without grouping these documents by genre, term counts and topic models may lead to incorrect interpretations.This study takes a step towards addressing this issue by focusing on genre classification within a digitized collection of European medical magazines in Swedish and German. We explore 2 scenarios: 1) leveraging the available web genre datasets for zero-shot genre prediction, 2) semi-supervised learning over the few-shot setup. This paper offers the first experimental insights in this direction.We find that 1) with a custom genre scheme tailored to historical dataset characteristics it is possible to effectively utilize categories from web genre datasets for cross-domain and cross-lingual zero-shot prediction, 2) semi-supervised training gives considerable advantages over few-shot for all models, particularly for the historical multilingual BERT.
Lexical Semantic Change Annotation with Large Language Models
Authors: Thora Hagen
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: This paper explores the application of state-of-the-art large language models (LLMs) to the task of lexical semantic change annotation (LSCA) using the historical German DURel dataset. We evaluate five LLMs, and investigate whether retrieval-augmented generation (RAG) with historical encyclopedic knowledge enhances results. Our findings show that the Llama3.3 model achieves comparable performance to GPT-4o despite significant parameter differences, while RAG marginally improves predictions for smaller models but hampers performance for larger ones. Further analysis suggests that our additional context benefits nouns more than verbs and adjectives, demonstrating the nuances of integrating external knowledge for semantic tasks.
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
Authors: Alexander Wuttke; Matthias Aßenmacher; Christopher Klamm; Max M. Lang; Quirin Würschinger; Frauke Kreuter
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Traditional methods for eliciting people’s opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents’ ability to voice their opinions in their own words, while conversational interviews provide deeper insights but are resource-intensive. This study explores the potential of replacing human interviewers with large language models (LLMs) to conduct scalable conversational interviews. Our goal is to assess the performance of AI Conversational Interviewing and to identify opportunities for improvement in a controlled environment. We conducted a small-scale, in-depth study with university students who were randomly assigned to a conversational interview by either AI or human interviewers, both employing identical questionnaires on political topics. Various quantitative and qualitative measures assessed interviewer adherence to guidelines, response quality, participant engagement, and overall interview efficacy. The findings indicate the viability of AI Conversational Interviewing in producing quality data comparable to traditional methods, with the added benefit of scalability. We publish our data and materials for re-use and present specific recommendations for effective implementation.
Prompting the Past: Exploring Zero-Shot Learning for Named Entity Recognition in Historical Texts Using Prompt-Answering LLMs
Authors: Crina Tudor; Beata Megyesi; Robert Östling
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: This paper investigates the application of prompt-answering Large Language Models (LLMs) for the task of Named Entity Recognition (NER) in historical texts. Historical NER presents unique challenges due to language change through time, spelling variation, limited availability of digitized data (and, in particular, labeled data), and errors introduced by Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) processes. Leveraging the zero-shot capabilities of prompt-answering LLMs, we address these challenges by prompting the model to extract entities such as persons, locations, organizations, and dates from historical documents. We then conduct an extensive error analysis of the model output in order to identify and address potential weaknesses in the entity recognition process. The results show that, while such models display ability for extracting named entities, their overall performance is lackluster. Our analysis reveals that model performance is significantly affected by hallucinations in the model output, as well as by challenges imposed by the evaluation of NER output.
LLMs for Translation: Historical, Low-Resourced Languages and Contemporary AI Models
Authors: Merve Tekgürler
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Large Language Models (LLMs) have demonstrated remarkable adaptability in performing various tasks, including machine translation (MT), without explicit training. Models such as OpenAI’s GPT-4 and Google’s Gemini are frequently evaluated on translation benchmarks and utilized as translation tools due to their high performance. This paper examines Gemini’s performance in translating an 18th-century Ottoman Turkish manuscript, Prisoner of the Infidels: The Memoirs of Osman Agha of Timișoara, into English. The manuscript recounts the experiences of Osman Agha, an Ottoman subject who spent 11 years as a prisoner of war in Austria, and includes his accounts of warfare and violence. Our analysis reveals that Gemini’s safety mechanisms flagged between 14% and 23% of the manuscript as harmful, resulting in untranslated passages. These safety settings, while effective in mitigating potential harm, hinder the model’s ability to provide complete and accurate translations of historical texts. Through real historical examples, this study highlights the inherent challenges and limitations of current LLM safety implementations in the handling of sensitive and context-rich materials. These real-world instances underscore potential failures of LLMs in contemporary translation scenarios, where accurate and comprehensive translations are crucial—for example, translating the accounts of modern victims of war for legal proceedings or humanitarian documentation.
Optimizing Cost-Efficiency with LLM-Generated Training Data for Conversational Semantic Frame Analysis
Authors: Shiho Matta; Yin Jou Huang; Fei Cheng; Hirokazu Kiyomaru; Yugo Murawaki
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Recent studies have shown that few-shot learning enables large language models (LLMs) to generate training data for supervised models at a low cost. However, for complex tasks, the quality of LLM-generated data often falls short compared to human-labeled data. This presents a critical challenge: how should one balance the trade-off between the higher quality but more expensive human-annotated data and the lower quality yet significantly cheaper LLM-generated data? In this paper, we tackle this question for a demanding task: conversational semantic frame analysis (SFA). To address this, we propose a novel method for synthesizing training data tailored to this complex task. Through experiments conducted across a wide range of budget levels, we find that smaller budgets favor a higher reliance on LLM-generated data to achieve optimal cost-efficiency.
Don’t stop pretraining! Efficiently building specialised language models in resource-constrained settings.
Authors: Sven Najem-Meyer; Frédéric Kaplan; Matteo Romanello
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Developing specialised language models for low-resource domains typically involves a trade-off between two specialisation strategies: adapting a general-purpose model through continued pretraining or retraining a model from scratch. While adapting preserves the model’s linguistic knowledge, retraining benefits from the flexibility of an in-domain tokeniser – a potentially significant advantage when handling rare languages. This study investigates the impact of tokenisation, specialisation strategy, and pretraining data availability using classical scholarship – a multilingual, code-switching and highly domain-specific field – as a case study. Through extensive experiments, we assess whether domain-specific tokenisation improves model performance, whether character-based models provide a viable alternative to subword-based models, and which specialisation strategy is optimal given the constraints of limited pretraining data. Contrary to prior findings, our results show that in-domain tokenisation does not necessarily enhance performance. Most notably, adaptation consistently outperforms retraining, even with limited data, confirming its efficiency as the preferred strategy for resource-constrained domains. These insights provide valuable guidelines for developing specialised models in fields with limited textual resources.
‘... like a needle in a haystack”: Annotation and Classification of Comparative Statements
Authors: Pritha Majumdar; Franziska Pannach; Arianna Graciotti; Johan Bos
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: We present a clear distinction between the phenomena of comparisons and similes along with a fine-grained annotation guideline that facilitates the structural annotation and assessment of the two classes, with three major contributions: 1) a publicly available annotated data set of 100 comparative statements; 2) theoretically grounded annotation guidelines for human annotators; and 3) results of machine learning experiments to establish how the–often subtle–distinction between the two phenomena can be automated.
Identifying Small Talk in Natural Conversations
Authors: Steffen Frenzel; Annette Hautli-Janisz
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Small talk is part and parcel of human interaction and is rather employed to communicate values and opinions than pure information. Despite small talk being an omnipresent phenomenon in spoken language, it is difficult to identify: Small talk is situated, i.e., for interpreting a string of words or discourse units, outside references such as the context of the interlocutors and their previous experiences have to be interpreted.In this paper, we present a dataset of natural conversation annotated with a theoretically well-motivated distillation of what constitutes small talk. This dataset comprises of verbatim transcribed public service encounters in German authorities and are the basis for empirical work in administrative policy on how the satisfaction of the citizen manifests itself in the communication with the authorities. We show that statistical models achieve comparable results to those of state-of-the-art LLMs.
Why Novels (Don’t) Break Through: Dynamics of Canonicity in the Danish Modern Breakthrough (1870-1900)
Authors: Alie Lassche; Pascale Feldkamp; Yuri Bizzoni; Katrine Baunvig; Kristoffer Nielbo
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: Recent studies suggest that canonical works possess unique textual profiles, often tied to innovation and higher cognitive demands. However, recent work on Danish 19th century literary novels has shown that some non-canonical works shared similar textual qualities with canonical works, underscoring the role of text-extrinsic factors in shaping canonicity. The present study examines the same corpus (more than 800 Danish novels from the Modern Breakthrough era (1870–1900)) to explore socio-economic and institutional factors, as well as demographic features, specifically, book prices, publishers, and the author’s nationality – in determining canonical status. We combine expert-based and national definitions of canon to set up a classification experiment to test the predictive power of these external features, and to understand how they relate to that of text-intrinsic features. We show that the canonization process is influenced by external factors – such as publisher and nationality – but that text-intrinsic features nevertheless maintain predictive power in a dynamic interplay of text and context.
Adapting Multilingual Embedding Models to Historical Luxembourgish
Authors: Andrianos Michail; Corina Raclé; Juri Opitz; Simon Clematide
Venue: NAACL 2025 (Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025))

Abstract: The growing volume of digitized historical texts requires effective semantic search using text embeddings. However, pre-trained multilingual models face challenges with historical content due to OCR noise and outdated spellings. This study examines multilingual embeddings for cross-lingual semantic search in historical Luxembourgish (LB), a low-resource language. We collect historical Luxembourgish news articles from various periods and use GPT-4o for sentence segmentation and translation, generating 20,000 parallel training sentences per language pair. Additionally, we create a semantic search (Historical LB Bitext Mining) evaluation set and find that existing models perform poorly on cross-lingual search for historical Luxembourgish. Using our historical and additional modern parallel training data, we adapt several multilingual embedding models through contrastive learning or knowledge distillation and increase accuracy significantly for all models. We release our adapted models and historical Luxembourgish-German/French/English bitexts to support further research.