All poetic forms come from somewhere. Prosodic templates can be copied for generations, altered by individuals, imported from foreign traditions, or fundamentally changed under the pressures of language evolution. Yet, these relationships are notoriously difficult to trace across languages and times. This article introduces an unsupervised method for detecting structural similarities in poems using local sequence alignment. The method relies on encoding poetic texts as strings of prosodic features using a four-letter alphabet; these sequences are then aligned to derive a distance measure based on weighted symbol (mis)matches. Local alignment allows poems to be clustered according to emergent properties of their underlying prosodic patterns. We evaluate method performance on a meter recognition tasks against strong baselines and show its potential for cross-lingual and historical research using three short case studies: (1) mutations in quantitative meter in classical Latin, (2) European diffusion of the Renaissance hendecasyllable and (3) comparative alignment of modern accentual-syllabic meters in 18–19th century Czech, German and Russian. We release an implementation of the algorithm as a Python package with an open license.

While the indirect evidence suggests that already in the early scholastic period the literary production based on records of oral teaching (so-called reportationes) was not uncommon, there are very few sources commenting on the practice. This article details the design of a study applying stylometric techniques of authorship attribution to a collection developed from reportationes – Stephen Langton’s Quaestiones Theologiae – aiming to uncover layers of editorial work and thus validate some hypotheses regarding the collection’s formation. Following Camps, Clérice, and Pinche (2021), I discuss the implementation of an HTR pipeline and stylometric analysis based on the most frequent words, POS tags, and pseudo-affixes. The proposed study will offer two methodological gains relevant to computational research on the scholastic tradition: it will directly compare performance on manually composed and automatically extracted data, and it will test the validity of transformer-based OCR and automated transcription alignment for workflows applied to scholastic Latin corpora. If successful, this study will provide an easily reusable template for the exploratory analysis of collaborative literary production stemming from medieval universities.

Language models have the ability to identify the characteristics of much shorter literary passages than was thought feasible with traditional stylometry. We evaluate authorship and genre detection for a new corpus of literary novels. We find that a range of LLMs are able to distinguish authorship and genre, but that different models do so in different ways. Some models rely more on memorization, while others make greater use of author or genre characteristics learned during fine-tuning. We additionally use three methods – direct syntactic ablation of input text and two means of studying internal model values – to probe one high-performing LLM for features that characterize styles. We find that authorial style is easier to characterize than genre-level style and is more impacted by minor syntactic decisions and contextual word usage. However, some traits like pronoun usage and word order prove significant for defining both kinds of literary style.

In this study, we perform a comprehensive evaluation of sentiment classification for German language data using three different approaches: (1) dictionary-based methods, (2) fine-tuned transformer models such as BERT and XLM-T and (3) various large language models (LLMs) with zero-shot capabilities, including natural language inference models, Siamese models and dialog-based models. The evaluation considers a variety of German language datasets, including contemporary social media texts, product reviews and humanities datasets. Our results confirm that dictionary-based methods, while computationally efficient and interpretable, fall short in classification accuracy. Fine-tuned models offer strong performance, but require significant training data and computational resources. LLMs with zero-shot capabilities, particularly dialog-based models, demonstrate competitive performance, often rivaling fine-tuned models, while eliminating the need for task-specific training. However, challenges remain regarding non-determinism, prompt sensitivity and the high resource requirements of large LLMs. The results suggest that for sentiment analysis in the computational humanities, where non-English and historical language data are common, LLM-based zero-shot classification is a viable alternative to fine-tuned models and dictionaries. Nevertheless, model selection remains highly context-dependent, requiring careful consideration of trade-offs between accuracy, resource efficiency and transparency.

undate is an ambitious, in-progress effort to develop a pragmatic Python package for computation and analysis of temporal information in humanistic and cultural data, with a particular emphasis on uncertain, incomplete, and imprecise dates and with support for multiple calendars. The development of undate is grounded in domain-specific work on digital and computational humanities projects from multiple institutions, including Shakespeare and Company Project, Princeton Geniza Project, and Islamic Scientific Manuscript Initiative. With increasing support for different formats and calendars, Undate aims to bridge technical gaps across different communities and methodologies. In this article, we describe the undate software package and the functionality of the core Undate and UndateInterval classes to work with dates and date intervals. We discuss why this software exists, how it expands on and generalizes prior work, how it compares to other approaches and tools, and its current limitations. We describe the development methodology used to create the software, our plans for active and continuing development, and the potential undate has to impact computational humanities research.

This article investigates how parliamentary debate in the Dutch House of Representatives (“Tweede Kamer”) (1945–1995) narrowed as MPs turned into domain specialists. We call this narrowing epistemic capture: a few experts progressively bound what can be said. To detect epistemic capture, we deploy a three-layer computational pipeline. Latent-Dirichlet topic modeling converts 8.2 million sentences into 250 semantic themes; Pointwise Mutual Information networks connect themes within six-month windows; Louvain clustering traces the birth, drift and endurance of topical communities.

Capture appears on every scale. Macro-level: network modularity almost doubles after 1960 while density falls, marking compartmentalized debate. Meso-level: cabinet turnovers act as “reset switches”: topic-neighborhood similarity drops in the half-year after a new coalition forms, then anneals along partisan lines. Micro-level: enduring communities – foreign policy, agriculture and education – lock topics and MPs together for decades, yet resistance to capture is visible in distinct contentious topics.

These multiscale patterns show how 20th-century Dutch parliamentary debate saw a rise of technical specialism that significantly constrained the breadth of political debate. Methodologically, the study demonstrates the value of structural (network) distant reading over purely lexical counts and offers a transferable workflow for measuring how democratic discourse undergoes structural transformations.

This article implements a critical method for assessing bias in large historical datasets that we term the “Environmental Scan.” The Environmental Scan sheds new light on newspaper collections by linking newly available “reference metadata” gathered from historical sources to existing full-text and catalogue metadata. The rise of computational methods in history and the social sciences, in tandem with newly “datafied” source materials, creates a challenge for researchers to adapt their existing critical practices to the increasing scale and complexity of computational research. To help address this challenge, the Environmental Scan situates big historical datasets in much greater context, including estimating what materials are missing, thereby revealing the ways digital collections can be “oligoptic” in nature. Using the British Newspaper Archive (BNA) as a case study, we diagnose the biases and imbalances in the digitised Victorian press. We determine which voices are under- or over-represented in relation to the political composition of the collection as well as its content and we trace the origins of these biases in the digitisation process. This article informs future interdisciplinary discussions about data bias and offers a conceptual model adaptable to diverse historical datasets. The Environmental Scan provides a more nuanced and accurate understanding of how newspaper data reflects past societies, making it a valuable tool for researchers.

Texts, whether literary or historical, exhibit structural and stylistic patterns shaped by their purpose, authorship and cultural context. Formulaic texts, which are characterized by repetition and constrained expression, tend to differ in their information content (as defined by Shannon) compared to more dynamic compositions. Identifying such patterns in historical documents, particularly multi-author texts like the Hebrew Bible, provides insights into their origins, purpose and transmission. This study aims to identify formulaic clusters: sections exhibiting systematic repetition and structural constraints, by analyzing recurring phrases, syntactic structures and stylistic markers. However, distinguishing formulaic from non-formulaic elements in an unsupervised manner presents a computational challenge, especially in high-dimensional and sample-poor data sets where patterns must be inferred without predefined labels.

To address this, we develop an information-theoretic algorithm leveraging weighted self-information distributions to detect structured patterns in text. Our approach directly models variations in sample-wise self-information to identify formulaicity. By extending classical discrete self-information measures with a continuous formulation based on differential self-information in multivariate Gaussian distributions, our method remains applicable across different types of textual representations, including neural embeddings under Gaussian priors.

Applied to hypothesized authorial divisions in the Hebrew Bible, our approach successfully isolates stylistic layers, providing a quantitative framework for textual stratification. This method enhances our ability to analyze compositional patterns, offering deeper insights into the literary and cultural evolution of texts shaped by complex authorship and editorial processes.

Location mentions in local news are crucial for examining issues like spatial inequalities, news deserts and the impact of media ownership on news diversity. However, while geoparsing – extracting and resolving location mentions – has advanced through statistical and deep learning methods, its use in local media studies remains limited and fragmented due to technical challenges and a lack of practical frameworks. To address these challenges, we identify key considerations for successful geoparsing and review spatially oriented local media studies, finding over-reliance on limited geospatial vocabularies, limited toponym disambiguation and inadequate validation of methods. These findings underscore the need for adaptable and robust solutions, and recent advancements in fine-tuned large language models (LLMs) for geoparsing offer a promising direction by simplifying technical implementation and excelling at understanding contextual nuances. However, their application to U.K. local media – marked by fine-grained geographies and colloquial place names – remains underexplored due to the absence of benchmark datasets. This gap hinders researchers’ ability to evaluate and refine geoparsing methods for this domain. To address this, we introduce the Local Media UK Geoparsing (LMUK-Geo) dataset, a hand-annotated corpus of U.K. local news articles designed to support the development and evaluation of geoparsing pipelines. We also propose an LLM-driven approach for toponym disambiguation that replaces fine-tuning with accessible prompt engineering. Using LMUK-Geo, we benchmark our approach against a fine-tuned method. Both perform well on the novel dataset: the fine-tuned model excels in minimising coordinate-error distances, while the prompt-based method offers a scalable alternative for district-level classification, particularly when relying on predictions agreed upon by multiple models. Our contributions establish a foundation for geoparsing local media, advancing methodological frameworks and practical tools to enable systematic and comparative research.

Cadastral data reveal key information about the historical organization of cities but are often non-standardized due to diverse formats and human annotations, complicating large-scale analysis. We explore as a case study Venice’s urban history during the critical period from 1740 to 1808, capturing the transition following the fall of the ancient Republic and the Ancien Régime. This era’s complex cadastral data, marked by its volume and lack of uniform structure, presents unique challenges that our approach adeptly navigates, enabling us to generate spatial queries that bridge past and present urban landscapes. We present a text-to-programs framework that leverages large language models to process natural language queries as executable code for analyzing historical cadastral records. Our methodology implements two complementary techniques: a SQL agent for handling structured queries about specific cadastral information, and a coding agent for complex analytical operations requiring custom data manipulation. We propose a taxonomy that classifies historical research questions based on their complexity and analytical requirements, mapping them to the most appropriate technical approach. This framework is supported by an investigation into the execution consistency of the system, alongside a qualitative analysis of the answers it produces. By ensuring interpretability and minimizing hallucination through verifiable program outputs, we demonstrate the system’s effectiveness in reconstructing past population information, property features and spatiotemporal comparisons in Venice.

In this article, we evaluate several large language models (LLMs) on a word-level translation alignment task between Ancient Greek and English. Comparing model performance to a human gold standard, we examine the performance of four different LLMs, two open-weight and two proprietary. We then take the best-performing model and generate examples of word-level alignments for further finetuning of the open-weight models. We observe significant improvement of open-weight models due to finetuning on synthetic data. These findings suggest that open-weight models, though not able to perform a certain task themselves, can be bolstered through finetuning to achieve impressive results. We believe that this work can help inform the development of more such tools in the digital classics and the computational humanities at large.

This article introduces a strategy for the large-scale corpus analysis of music audio recordings, aimed at identifying long-term trends and testing hypotheses regarding the repertoire represented in a given corpus. Our approach centers on computing evolution curves (ECs), which map style-relevant features, such as musical complexity, onto historical timelines. Unlike traditional approaches that rely on sheet music, we use audio recordings, leveraging their widespread availability and the performance nuances they capture. We also emphasize the benefits of pitch-class features based on deep learning, which improve the robustness and accuracy of tonal complexity measures compared to traditional signal processing methods. Addressing the frequent lack of exact work dates (year of composition) in historical corpora, we propose a heuristic method that aligns works with timelines using composers’ life dates. This method effectively preserves historical trends with minimal deviation compared to using actual work dates, as validated against available metadata from the Carus Audio Corpus, which spans 450 years of choral and sacred music and contains 5,729 tracks with detailed metadata. We demonstrate the utility of our strategy through case studies of this corpus, showing how ECs provide insights into stylistic developments that confirm expectations from musicology, thus highlighting the potential of computational studies in this field. For example, we observe a steady increase in tonal complexity from the Renaissance through the Baroque period, stable complexity levels in the 19th and 20th centuries, and consistently higher complexity in minor-key works compared to major-key works. Our visualizations also reveal that vocal music was more complex than instrumental music in the 18th century, but less complex in the 20th century. Finally, we conduct comparative analyses of individual composers, exploring how historical and biographical contexts may have influenced their works. Our findings highlight the potential of this strategy for computational corpus studies in musicological research.

We present Digital Collections Explorer, a web-based, open-source exploratory search platform that leverages Contrastive Language-Image Pre-training for enhanced visual discovery of digital collections. Our Digital Collections Explorer can be installed locally and configured to run on a visual collection of interest on disk in just a few steps. Building upon recent advances in multimodal search techniques, our interface enables natural language queries and reverse image searches over digital collections with visual features. This article describes the system’s architecture, implementation and application to various cultural heritage collections, demonstrating its potential for democratizing access to digital archives, especially those with impoverished metadata. We present case studies with maps, photographs and PDFs extracted from web archives in order to demonstrate the flexibility of the Digital Collections Explorer, as well as its ease of use. We demonstrate that the Digital Collections Explorer scales to hundreds of thousands of images on a MacBook Pro with an M4 chip. Lastly, we host a public demo of Digital Collections Explorer.

The exploration and retrieval of information from large, unstructured document collections remain challenging. Unsupervised techniques, such as clustering and topic modeling, provide only a coarse overview of thematic structure, while traditional keyword searches often require extensive manual effort. Recent advances in large language models and retrieval-augmented generation (RAG) introduce new opportunities by enabling focused retrieval of relevant documents or chunks tailored to a user’s query. This allows for dynamic, chat-like interactions that streamline exploration and improve access to pertinent information. This article introduces Topic-RAG, a chat engine that integrates topic modeling with RAG to support interactive and exploratory document retrieval. Topic-RAG uses BERTopic to identify the most relevant topics for a given query and restricts retrieval to documents or chunks within those topics. This targeted strategy enhances retrieval relevance by narrowing the search space to thematically aligned content. We utilize the pipeline on 4,711 articles related to nuclear energy from the Impresso historical Swiss newspaper corpus. Our experimental results demonstrate that Topic-RAG outperforms a baseline RAG architecture that does not incorporate topic modeling, as measured by widely recognized metrics, such as BERTScore (including Precision, Recall and F1), ROUGE and UniEval. Topic-RAG also achieves improvements in computational efficiency for both single and batch query processing. In addition, we performed a qualitative analysis in collaboration with domain experts, who assessed the system’s effectiveness in supporting historically grounded research. Although our evaluation is focused on historical newspaper articles, the proposed approach more generally integrates topic information to enhance retrieval performance within a transparent and user-configurable pipeline effectively. It supports the targeted retrieval of contextually rich and semantically relevant content while also allowing users to adjust key parameters such as the number of documents retrieved. This flexibility provides greater control and adaptability to meet diverse research needs in historical inquiry, literary analysis and cultural studies. Due to copyright restrictions, the raw data cannot be publicly shared. Data access instructions are provided in the repository, and the replication code is available on GitHub: https://github.com/KeerthanaMurugaraj/Topic-RAG-for-Historical-Newspapers.

This article explores the potential of large language models (LLMs), particularly through the use of contextualized word embeddings, to trace the evolution of scientific concepts. It thus aims to extend the potential of LLMs, currently transforming much of humanities research, to the specialized field of history and philosophy of science. Using the concept of the virtual particle – a fundamental idea in understanding elementary particle interactions – as a case study, we domain-adapted a pretrained Bidirectional Encoder Representations from Transformers model on nearly a century of Physical Review publications. By employing semantic change detection techniques, we examined shifts in the meaning and usage of the term “virtual.” Our analysis reveals that the dominant meaning of “virtual” stabilized after the 1950s, aligning with the formalization of the virtual particle concept, while the polysemy of “virtual” continued to grow. Augmenting these findings with dependency parsing and qualitative analysis, we identify pivotal historical transitions in the term’s usage. In a broader methodological discussion, we address challenges such as the complex relationship between words and concepts, the influence of historical and linguistic biases in datasets, and the exclusion of mathematical formulas from text-based approaches.

The volumes of historical data locked behind unstructured formats have long been a challenge for researchers in the computational humanities. While optical character recognition (OCR) and natural language processing have enabled large-scale text mining projects, the irregular formatting, inconsistent terminology and evolving printing practices complicate automated parsing and information extraction efforts for historical documents. This study explores the potential of large language models (LLMs) in processing and structuring irregular and non-standardized historical materials, using the U.S. Department of Agriculture’s Plant Inventory books (1898–2008) as a test case. Given the frequent evolution of these historical records, we implemented a pipeline combining OCR, custom segmentation rules and LLMs to extract structured data from the scanned texts. It provides an example of how incorporating LLMs into data-processing pipelines can enhance the accessibility and usability of historical and archival materials for scholars.

From the early use of TF-IDF to the high-dimensional outputs of deep learning, vector space embeddings of text, at a scale ranging from token to document, are at the heart of all machine analysis and generation of text. In this article, we present the first large-scale comparison of a sampling of such techniques on a range of classification tasks on a large corpus of current literature drawn from the well-known Books3 data set. Specifically, we compare TF-IDF, Doc2vec and several Transformer-based embeddings on a variety of text-specific tasks. Using industry-standard BISAC codes as a proxy for genre, we compare embeddings in their ability to preserve information about genre. We further compare these embeddings in their ability to encode inter- and intra-book similarity. All of these comparisons take place at the book “chunk” (1,024 tokens) level. We find Transformer-based (“neural”) embeddings to be best, in the sense of their ability to respect genre and authorship, although almost all embedding techniques produce sensible constructions of a “literary landscape” as embodied by the Books3 corpus. These experiments suggest the possibility of using deep learning embeddings not only for advances in generative AI, but also a potential tool for book discovery and as an aid to various forms of more traditional comparative textual analysis.