Malta

In this paper, we bridge computational linguistics with historical methods to explore the potential of topic modeling in historical newspapers. Our case study focuses on British and American newspapers published in the second half of the 20th century that debate issues of Greek tourism, but our method can be transposed to any diachronic data. We demonstrate that Non-negative Matrix Factorization (NFM) can generate interpretable topics within the historical period under examination providing a tangible example of how computational text analysis can assist historical research. The contribution of our work is two-fold; first, the extracted topics are evaluated both by a computational linguist and by a historian highlighting the crucial role of domain experts when interpreting topic modeling outputs. Second, the extracted topics are contextualized within the historical and political environment in which they appear, providing interesting insights about the historical representations of Greek tourism over the years, and about the development and the hallmarks of American and British tourism in Greece across different historical periods (from 1945 to 1989). The comparative analysis between the American and the British press reveals interesting insights including similar responses to specific events as well as notable differences between British and American tourism to Greece during the historical periods under examination. Overall, the results of our analysis can provide valuable information for academics and researchers in the field of (Digital) Humanities and Social Sciences, as well as for stakeholders in the tourism industry.

EACL 2024

Historical Portrayal of Greek Tourism through Topic Modeling on International Newspapers

greek tourism

historical newspapers

topic modeling

Dear Delegates, 

On behalf of the organizing committee at UCF Rosen College of Hospitality Management and Elsevier, I have the greatest pleasure to welcome you to the **6th World Research Summit for Tourism and Hospitality: Resilience, Adaptability, and Innovation.** 

Though we cannot host you this year owing to Covid-19 at our beautiful resort-like campus in Orlando, Florida, we believe this virtual and live-streamed conference will provide an interactive, dynamic, and shared international platform for global academics and practitioners to discuss how to enhance research applications and knowledge management through resilience, adaptability, digital intelligence, innovation, and smart knowledge in bringing our industry and research back to normal. This summit particularly offers unique opportunities to unfold and embrace various repercussions of COVID-19 pandemic with various stakeholders, including academic researchers, industry professionals, and government officials, through dialogues on how to better utilize research outputs for the benefits of the industry, in a resilient, intelligent and innovative way. 

I hope you will agree that we have put together a wonderful program including keynote speeches, panel discussions, concurrent presentations, poster sessions, and best paper recognition and award ceremony. We hope you will take advantage of these activities and engage yourself with great learning and sharing opportunities during the two days of the Summit. Thank you for joining us and we look forward to our discussions! 

Sincerely, 
Dr. Youcheng Wang 
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TOUR 2021

This conference provides an interactive, dynamic, and shared international platform for global academics and practitioners to discuss how to enhance research applications and knowledge management through resilience, adaptability, digital intelligence, innovation, and smart knowledge in bringing our industry and research back to normal. This summit particularly offers unique opportunities to unfold and embrace various repercussions of COVID-19 pandemic with various stakeholders, including academic researchers, industry professionals, and government officials, through dialogues on how to better utilize research outputs for the benefits of the industry, in a resilient, intelligent and innovative way.

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.

The quality of automatic transcription of heritage documents, whether from printed, manuscripts or audio sources, has a decisive impact on the ability to search and process historical texts. Although significant progress has been made in text recognition (OCR, HTR, ASR), textual materials derived from library and archive collections remain largely erroneous and noisy. Effective post-transcription correction methods are therefore necessary and have been intensively researched for many years. As large language models (LLMs) have recently shown exceptional performances in a variety of text-related tasks, we investigate their ability to amend poor historical transcriptions. We evaluate fourteen foundation language models against various post-correction benchmarks comprising different languages, time periods and document types, as well as different transcription quality and origins. We compare the performance of different model sizes and different prompts of increasing complexity in zero and few-shot settings. Our evaluation shows that LLMs are anything but efficient at this task. Quantitative and qualitative analyses of results allow us to share valuable insights for future work on post-correcting historical texts with LLMs.

Post-Correction of Historical Text Transcripts with Large Language Models: An Exploratory Study

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities. In this work, we explore stylistic representations of characters built by encoding their quotes with off-the-shelf pretrained authorship verification models in a large corpus of English novels (the Project Dialogism Novel Corpus). Results suggest that the combination of stylistic and topical information captured in these models accurately distinguish characters among each other, but does not necessarily improve over semantic-only models when attributing quotes. However, these results vary among novels and more investigation of stylometric models particularly tailored for literary texts and the study of characters should be conducted.

Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution

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.

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

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.

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Post-Correction of Historical Text Transcripts with Large Language Models: An Exploratory Study

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