Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Bibliographic record
Abstract
A lot of interesting work revolves around the computational analysis of prose.We have papers which present tools for scholars in Digital Humanities, and more specific studies of certain phenomena or of particular novels.Andre Blessing, Nora Echelmeyer, Markus John and Nils Reiter present an endto-end environment intended to help analyze relations between entities in a document in a principled way.Evgeny Kim, Sebastian Padó and Roman Klinger adopt lexicon-based methods to the study of the emotional trajectory of novels, and compare their findings across five genres.Stefania Degaetano-Ortlieb and Elke Teich outline a generic data-driven method of tracking intra-textual variation, showing how information-theoretic measures allow the detection of both topical and stylistic patterns of variation.Liviu Dinu and Ana Sabina Uban verify if characters of a given novel are believable, using methods established in the authorship attribution community.They present the preliminary results for the novel Les Liaisons Dangereuses.Conor Kelleher and Mark Keane describe an experiment in distant reading applied to a post-modern novel with non-linear structure, Wittgenstein's Mistress by David Markson.The paper contrasts the analysis which arises from the distant read with David Foster Wallace's "manual" analysis.A good portion of our workshop is devoted to historical, low-resource or non-standard languages.Amrith Krishna, Pavankumar Satuluri and Pawan Goyal write about challenges of working with Sanskrit manuscripts.They release a dataset for the segmentation of Sanskrit words.Nina Seemann, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke and Eyke Hüllermeier share the experience of annotating texts in Middle Low German.It turns out that the process is fraught with uncertainties; the Authors discuss them and describe lessons learned.Next, we have a paper by Maria Sukhareva, Francesco Fuscagni, Johannes Daxenberger, Susanne Görke, Doris Prechel and Iryna Gurevych.In their experiments, they apply distant supervision to the building of a part-of-speech tagger for Hittite.Unsurprisingly, no annotated corpora exist for this ancient language.iii Émilie Pagé-Perron, Maria Sukhareva (yes!), Ilya Khait and Christian Chiarcos are no less ambitious.They describe experiments in machine translation of Sumerian texts of an administrative or legal nature.The aim is to make those texts available to a wider audience.Géraldine Walther and Benoît Sagot talk about a productive synergy between fully manual and semi-automatic process when building a corpus of Romansh Tuatschin, a dialect of one of the official languages in southwestern Switzerland.Two more papers complete this palette of topics.Maria Pia di Buono proposes an ontology-based method of extracting nominal compounds in the domain of cultural heritage.Maciej Ogrodniczuk and Mateusz Kopeć explore modern political discourse in the context of Twitter.They present a series of experiments in on-the-fly analysis of the lexical, topical and visual aspects of political tweets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.102 | 0.041 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".