The Centre for Digital Humanities and Arts in Iceland is pleased to announce the second iteration of its lecture series, this time bearing the title Lunch with Digital Humanities.
When: Tuesdays, 12:00 GMT
Where: University of Iceland, Veröld 108
This series brings together a range of experts working across digital humanities and the arts, offering informal, engaging talks designed for a broad audience. The focus is on sharing ideas and perspectives in a clear and accessible way, making the field approachable to anyone with an interest, no prior knowledge required.
Talks will also be livestreamed on MSHL’s YouTube channel, making them accessible both on-site and online.
You can find the recordings and materials from our inaugural lecture series, Digital Humanities After Hours, here.
Full schedule below.
The lecture will be livestreamed here.
Alexander Peter Pfaff, University of Iceland
How Many Ways Can You Say It? Measuring Language Diversity with Digital Methods
Understanding how languages vary in the way they build expressions is essential for studying texts across periods and languages, yet traditional descriptions capture only a fraction of this diversity. Patternization is an approach that combines traditional text analysis and corpus linguistics with mathematical methods into a Python tool that allows us to explore such syntactic variation: it treats phrases as sequences of category labels and compares the patterns that actually appear with the full range of patterns that could appear. This task is hardly feasible if performed manually, illustrating how digital methods can reveal nuances otherwise invisible to the human eye.
The lecture will be livestreamed here.
Eiríkur Smári Sigurðarson, University of Iceland, MSHL
Tengjum allt við allt!
Hvernig getum við breytt einangruðum gagnasöfnum í lifandi og samtengdan þekkingarvef? Í þessu erindi er fjallað um tækifæri sem verða til þegar menningarleg gögn eru stafræn og tengd með merkingarfræðilegri tækni. Með því að nýta aðferðafræði tengdra gagna (e. Linked Data) má rjúfa múra á milli ólíkra stofnana og verkefna, þannig að upplýsingar flæði á milli kerfa.
Með innblæstri frá hinu finnska Sampo-módeli, skoðum við hvernig hægt er að smíða stafrænt vistkerfi þar sem handrit, myndlist, örnefni og sögulegar persónur mætast í einu vistkerfi. Markmiðið er að hætta að líta á gögn sem stakar eyjar og byrja að tengja allt við allt. Við ætlum ekki bara að varðveita menninguna, heldur gera hana aðgengilega og gagnvirka.
The lecture will be livestreamed here.
Trausti Dagsson, Árni Magnússon Institute for Icelandic Studies
Ísmús: Víðfeðmur vefur þjóðsagna, tónlistar og radda fortíðarinnar
Vefurinn Ísmús er samstarfsverkefni Árnastofnunar og Tónlistarsafns Íslands sem nú er hluti af Landsbókasafni. Á vefnum má nálgast upptökur úr þjóðfræðisafni Árnastofnunar ásamt gríðarmiklum upplýsingum um tónlistarsögu Íslands. Í fyrirlestrinum verður fjallað um hvernig önnur stór gagnasöfn hafa runnið saman við Ísmús og þannig skapað víðfeðmt yfirlit yfir þjóðfræði, sagnir, ævintýri, alþýðukveðskap, þjóðlög og hljómsveitir. Einnig verður sýnt hvernig sjálfvirkri talgreiningu var beitt á upptökur í þjóðfræðisafninu og hvernig önnur rafræn söfn tengjast Ísmús–eins og Nafnið.is, Bækur.is og Handrit.is.
The lecture will be livestreamed here.
Ondřej Tichý, Charles University, Prague
Benchmarking Large Language Models for DH Research
Recent advances in Large Language Models (LLMs) have shown that they can increasingly replace traditional NLP techniques across a range of linguistic tasks, such as part-of-speech tagging or orthographic normalization, and can even approach or surpass human annotators in tasks such as speech-act classification or genre annotation. However, the results of previous studies are often difficult to replicate, given the wide range of factors influencing LLM output, its inherent stochasticity, and the breakneck lifecycle of the individual models. Consequently, state-of-the-art results, their systematic comparison and generalization remain challenging.
In this paper, I propose a set of guidelines and Python scripts designed to make benchmarking LLMs on linguistic tasks more accessible, reproducible, and comparable. I also conduct several benchmarking experiments using this methodology to validate it and to identify best practices for applying LLMs to linguistic research, as well as to determine which current models perform best in specific tasks.
The proposed guidelines define input and output data structures for a variety of linguistic tasks, recommend parameter settings (e.g. temperature, top_p, chain-of-thought reasoning, retrieval-augmented generation), and outline how to interpret outputs such as self-reported confidence scores and token-level probabilities. The accompanying scripts enable researchers to (re)run tests on new tasks or new models and to generate comparable reports. While model fine-tuning is outside the scope of our framework, we support both zero- and few-shot prompting, allowing users to provide ground-truth data for evaluation and, optionally, as few-shot examples.
In my own tests, I will focus on tasks that have not been largely solved by NLP (avoiding e.g. PoS tagging in English) and that are commonly performed by empirical and more specifically corpus linguists. While most tasks target Present-Day English, I also investigate how LLMs handle low-resource languages and non-standardized varieties by including Czech and earlier stages of English. The selected tasks range from morphological and syntactic classification (e.g. identifying nominal number in Old English or the syntactic role of non-finite verbs in Present-Day English) to pragmatic annotation (e.g. contextual functions of like), semantic disambiguation, and historical spelling normalization.
I benchmark both major commercial models (e.g. ChatGPT, Gemini, Claude) and leading open-source or smaller models (e.g. gpt-oss, LLaMA, DeepSeek, Mistral), including different quantizations and configurations (leveraging the resources of the e-infra.cz research infrastructure). This enables evaluation not only of their performance but also of factors such as cost, accessibility, and data security, as well as testing claims such as smaller models outperforming larger ones on simple binary classifications.
My preliminary findings suggest that some linguistic tasks, such as text normalization and basic morphological classification, are already well-suited to LLM applications. In contrast, more complex tasks requiring extensive context and elaborate hierarchical categorization, such as discourse and pragmatic annotation, still fall significantly short of human performance.
More information soon
Eydís Huld Magnúsdóttir, Tiro ehf.
Fjársjóðsleit með máltækni
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Timothy Liam Waters, Ohio State University
Title TBA
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Sigríður Regína Sigurþórsdóttir, University of Iceland & National Gallery of Iceland
Title TBA
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Hannes Högni Vilhjálmsson, Reykjavík University
Experiencing another time and place in 3D: The Historical Postal Route project
More information soon
Paola Peratello, École nationale des chartes – PSL, Paris
Title TBA
