Malta

This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect ``strategies&#39;&#39; to achieve an informationally dense literary style.

EACL 2024

Perplexing Canon: A study on GPT-based perplexity of canonical and non-canonical literary works

nominal style.

canonicity

literary quality

perplexity

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect ``strategies'' to achieve an informationally dense literary style.

workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Named Entity Recognition (NER) is an important step in many Natural Language Processing tasks. The existing state-of-the-art NER systems are however typically developed based on contemporary data, and not very well suited to analyzing historical text. In this paper, we present a comparative analysis of the performance of several language models when applied to Named Entity Recognition for historical Swedish text. The source texts we work with are documents from Swedish labour unions dating from the 19th and 20th century. We experiment with three off-the-shelf models for contemporary Swedish text, and one language model built on historical Swedish text that we fine-tune with labelled data for adaptation to the NER task. Lastly, we propose a hybrid approach by combining the results of two models in order to maximize usability. We show that, even though historical Swedish is a low-resource language with data sparsity issues affecting overall performance, historical language models still show very promising results. Further contributions of our paper are the release of our newly trained model for NER of historical Swedish text, along with a manually annotated corpus of over 650 named entities.

People and Places of the Past - Named Entity Recognition in Swedish Labour Movement Documents from Historical Sources

Part-of-speech tagging is a foundational aspect of natural language processing, transcending mere linguistic functions. However, taggers optimized for Classical Latin struggle when faced with diverse linguistic eras shaped by the language's evolution. Exploring 16th-century Latin from the correspondence and assessing five Latin treebanks, we focused on carefully evaluating tagger accuracy and refining GPT models for improved performance in this nuanced linguistic context. Our discoveries unveiled the competitive accuracies achieved by GPT's Large Language Models, particularly after fine-tuning. Notably, our best fine-tuned model soared to an average accuracy of 88.99% over the treebank data, underscoring the remarkable adaptability and learning capabilities when fine-tuned to the specific intricacies of Latin texts. Our primary aim is to strengthen part-of-speech taggers' adaptability across different periods. We establish solid groundwork for using Large Language Models in specific natural language processing tasks where part-of-speech tagging is often employed as a pre-processing step. This work significantly advances the use of modern language models in interpreting historical language, bridging the gap between past linguistic epochs and modern computational linguistics.

Part-of-Speech Tagging of 16th-Century Latin with GPT

This paper discusses two approaches to the diachronic normalization of Polish texts: a rule-based solution that relies on a set of handcrafted patterns, and a neural normalization model based on the text-to-text transfer transformer architecture. The training and evaluation data prepared for the task are discussed in detail, along with experiments conducted to compare the proposed normalization solutions. A qualitative analysis of both approaches is made.

Two Approaches to Diachronic Normalization of Polish Texts

Digital archive collections that have been contributed by communities, known as community-generated digital content (CGDC), are important sources of historical and cultural knowledge. However, CGDC items are not easily searchable due to semantic information being obscured within their textual metadata. In this paper, we investigate the extent to which state-of-the-art, general-domain entity linking (EL) models (i.e., BLINK, EPGEL and mGENRE) can map named entities mentioned in CGDC textual metadata, to Wikidata entities. We evaluate and compare their performance on an annotated dataset of CGDC textual metadata and provide some error analysis, in the way of informing future studies aimed at enriching CGDC metadata using entity linking methods.

Enriching the Metadata of Community-Generated Digital Content through Entity Linking: An Evaluative Comparison of State-of-the-Art Models

The application of text mining methods is gaining increasing attention not only in the Digital Humanities (DH) and Computational Social Sciences (CSS) in general, but also in vocational education and training (VET) research. The use of algorithms promises to enable research on corpora that are simply too large for manual methods. The challenge is that concepts such as occupations or skills, which play an important role in VET research, are abstract in principle, but algorithms require concrete instructions for action (in terms of rules or annotated examples), which have to be negotiated during the process of operationalisation. Using the concept of occupations, we will compare two approaches for automatically identifying the concept in a large corpus. We will take debates from the German Bundestag and we will compare a dictionary-based approach with a BERT fine-tuning, two methods that are widely used in the context of DH and CSS.

Recognising Occupational Titles in German Parliamentary Debates

We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.

Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses

Many collections of digitized newspapers suffer from poor OCR quality, which impacts readability, information retrieval, and analysis of the material. Errors in OCR output can be reduced by applying machine translation models to "translate" it into a corrected version. Although transformer models show promising results in post-OCR correction and related tasks in other languages, they have not yet been explored for correcting OCR errors in Swedish texts. This paper presents a post-OCR correction model for Swedish 19th to 21th century newspapers based on the pre-trained transformer model ByT5. Three versions of the model were trained on different mixes of training data. The best model, which achieved a 36% reduction in CER, is made freely available and will be integrated into the automatic processing pipeline of Språkbanken Text, a Swedish language technology infrastructure containing modern and historical written data.

Post-OCR Correction of Digitized Swedish Newspapers with ByT5

In this paper we present the Kronieken Corpus, a new digital collection of 204 chronicles written in Dutch/Flemish between 1500 and 1850, which have been scanned, transcribed and annotated with named entities, dates, pages and a smaller part with sources and attributions. The texts belong to 308 physical volumes and contain between 23 and 24 million words. 107 chronicles, or 178 chronicle volumes, collected from 39 different archives and libraries in The Netherlands and Belgium and transcribed by volunteers had never been transcribed or published before. The result is a unique enriched historical text corpus of original hand-written, non-canonical and non-fiction text by lay people from the early modern period.

The Kronieken Corpus: an Annotated Collection of Dutch/Flemish Chronicles from 1500-1850

Identifying direct speech in literary fiction is challenging for cases that do not mark speech segments with quotation marks. Such efforts have previously been based either on smaller manually annotated gold data or larger automatically annotated silver data, extracted from works with quotation marks. However, no comparison has so far been made between the performance of these two types of training data. In this work, we address this gap. We further explore the effect of different types of typographical speech marking and of using evaluation metrics of different granularity. We perform experiments on Swedish literary texts and find that using gold and silver data has different strengths, and gives different results on span-level and token-level metrics. If the training data contains some data that matches the speech marking of the target, that is generally sufficient for achieving good results, but it does not seem to hurt if the training data also contains other markings.

Direct Speech Identification in Swedish Literature – an Exploration of Training Data Type, Typographical Markers, and Evaluation Granularity

We present a novel corpus consisting of orthographically variant words found in works of 19th century U.S. literature annotated with their corresponding "standard" word pair. We train a set of neural edit distance models to pair these variants with their standard forms, and compare the performance of these models to the performance of a set of neural edit distance models trained on a corpus of orthographic errors made by L2 English learners. Finally, we analyze the relative performance of these models in the light of different negative training sample generation strategies, and offer concluding remarks on the unique challenge literary orthographic variation poses to string pairing methodologies.

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