---
title: "Machines in the media: semantic change in the lexicon of"
description: "The paper asks whether computational tools can follow the meaning shifts that 19th-century… 11 references, cited by 2 papers here."
authors: ["Nilo Pedrazzini", "Barbara McGillivray"]
year: 2022
venue: "Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities"
source: "https://aclanthology.org/2022.nlp4dh-1.12/"
doi: "10.18653/v1/2022.nlp4dh-1.12"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pedrazzini-2022-machines-media-semantic-change-lexicon/"
---
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# Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers

**Authors:** Nilo Pedrazzini and [Barbara McGillivray](https://md.research.elamur.ai/free/en/authors/barbara-mcgillivray/) · **Year:** 2022 · **Venue:** [Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities](https://md.research.elamur.ai/free/en/venues/nlp4dh/) ([International Conference on Natural Language Processing for the Digital Humanities 2022](https://md.research.elamur.ai/free/en/venues/nlp4dh/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.nlp4dh-1.12/) · [PDF](https://aclanthology.org/2022.nlp4dh-1.12.pdf) · [DOI 10.18653/v1/2022.nlp4dh-1.12](https://doi.org/10.18653/v1/2022.nlp4dh-1.12) · [OpenAlex](https://openalex.org/W4404783017)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether computational tools can follow the meaning shifts that 19th-century industrialisation caused in English words about machines and transport, and whether these tools agree with traditional philological accounts. The authors combined two digitised British newspaper collections (about 4.6 billion tokens, 1801–1920) and divided them into decades. They trained a separate Word2Vec model for each decade and aligned the resulting spaces with Orthogonal Procrustes. For each word, they measured the cosine similarity between every decade's vector and the 1910s vector. They then ran the PELT changepoint algorithm on these similarity curves. Detected shifts were checked against nearest neighbours, Görlach's (1999) history of 19th-century English and OED sense dates. Most detections agreed with the expert literature. For coach, the models picked up the railway-carriage sense in the same decade as its first OED attestation, and the data suggest this use was not only American. The study shows that large OCR'd newspaper archives can support semantic-change research in digital humanities when validation is done carefully.

## Key points
- **Data:** Heritage Made Digital (12 titles, about 2.3B tokens) plus Living with Machines newspapers (107 titles, about 2.3B tokens). Text was lowercased, punctuation and OCR line breaks were removed, and NLTK stop words were dropped.
- **Model selection:** a grid search on the 1821–1830 slice scored candidate settings by mean cosine similarity of five OED/Historical Thesaurus synonym pairs. The winning configuration was skip-gram, 5 epochs, 200 dimensions, window 3 and min count 1.
- **Changepoint detection:** PELT from the ruptures library, run with jump 1 and penalties of 0.5 and 0.25.
  - At penalty 0.5, eight words were flagged: coach, gear, traffic and train (1830), stamp (1840), fellow and railway (1860), and matches (1880).
  - At penalty 0.25, wheel was also flagged (1810, 1880), and traffic gained a second changepoint in 1860.
- **Mismatches with the literature:**
  - Fellow and wheel, which Görlach calls stable, were still flagged.
  - Bulb got no changepoint. The authors attribute this to its tangled polysemy: it already had senses in anatomy, botany and electricity before 1800.
- **Negative control:** the words from the synonym pairs, which the OED treats as stable, produced no changepoints at penalty 0.5, apart from identical.

## Interesting ideas
- Testing the detector on words known to be stable (here, the synonym-pair vocabulary) shows how often it raises false alarms, and that rate is what makes its positive findings believable.
- Instead of picking hyperparameters with an abstract loss, they used a few hand-checked synonym pairs with known relations as a cheap intrinsic benchmark.
- Every decade is compared with one fixed reference period (the latest) rather than only with its neighbour, which turns change into a single curve that a changepoint algorithm can segment.
- A numeric detection is only accepted as meaningful after inspecting nearest neighbours and matching them to dated dictionary senses, so every flagged change comes with a human-readable reason.
- The authors note that heavily polysemous words with many pre-existing senses blur a single-vector signal, which is why bulb showed no clear changepoint.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Any automatic signal Mova uses, such as witness-parser disagreement or a repair-rule trigger, could be run on a held-out set where no change or error is expected, so its false-positive rate is reported alongside its hits.
- **I2** (learn): A small curated list of pairs known to share or lack a VerbNet class or WordNet relation could serve as a fast sanity check when choosing settings for lexicon-induction or silver-data steps.
- **I3** (world): Tracking an entity's state against a fixed anchor point in the story, not only step to step, could help locate the exact event where a holding or attribute shifted.
- **I4** (world): When Mova's compiled lexicon assigns or changes a word sense, linking that decision to concrete WordNet/FrameNet evidence would keep the step explainable and auditable.
- **I5** (frames): Sense-sensitive role assignment should flag highly polysemous predicates for frame-level disambiguation instead of trusting a single averaged representation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The Alan Turing Institute](https://md.research.elamur.ai/free/en/labs/the-alan-turing-institute/) — Research institute, London, United Kingdom · 31 works · 4,535 citations
- [King's College London](https://md.research.elamur.ai/free/en/universities/king-s-college-london/) — University, London, United Kingdom · 46 works · 1,714 citations

📚 Shelves (3): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [📚 Literature and Literary Theory](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/literature-and-literary-theory/) · 541 works

🧭 Topics (1): [⏳ Historical linguistics & language change](https://md.research.elamur.ai/free/en/topics/language-change/) · 250 works

## Cite

Cite as: Nilo Pedrazzini and Barbara McGillivray. 2022. Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers. Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{pedrazzini2022machines,
  title = {{Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers}},
  author = {Nilo Pedrazzini and Barbara McGillivray},
  booktitle = {Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities},
  year = {2022},
  doi = {10.18653/v1/2022.nlp4dh-1.12},
  url = {https://doi.org/10.18653/v1/2022.nlp4dh-1.12}
}
```

- [← previous Lexical variation in English language podcasts, editorial media, and social media](https://md.research.elamur.ai/free/en/papers/karlgren-2022-lexical-variation-english-language-podcasts/)
- [next → Meta-Learning based Deferred Optimisation for Sentiment and Emotion aware Multi-modal Dialogue Act Classification](https://md.research.elamur.ai/free/en/papers/saha-2022-meta-learning-based-deferred-optimisation/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/8/)

## References

11 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Kaspar Beelen, Jon Lawrence, Daniel C. S. Wilson, and David Beavan. 2022. Bias and representativeness in digitized newspaper collections: Introducing the environmental scan. Digital Scholarship in the Humanities, pages 1–22. [↗](https://doi.org/10.1093/llc/fqac037)
2. Alexander Bergs and Laurel J. Brinton, editors. 2012. English Historical Linguistics. Handbücher zur Sprach- und Kommunikationswissenschaft / Handbooks of Linguistics and Communication Science 34.1. De Gruyter Mouton, Berlin, Boston. [find](https://scholar.google.com/scholar?q=Alexander+Bergs+and+Laurel+J%2E+Brinton%2C+editors%2E+2012%2E+English+Historical+Linguistics%2E+Handbu%CC%88cher+zur+Sprach%2D+und+Kommunikationswissenschaft+%2F+Handbooks+of+Linguistics+and+Communication+Science+34%2E1%2E+)
3. Ingegerd Bäcklund. 2006. [Modifiers describing women and men in nineteenth-century English, pages 17–55](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/). Cambridge University Press, Cambridge.
4. Christian Kay and Kathryn Allan. 2015. English Historical Semantics. Edinburgh University Press, Edinburgh. [↗](https://doi.org/10.1515/9780748644797)
5. Daniel van Strien, Kaspar Beelen, Mariona Ardanuy, Kasra Hosseini, Barbara McGillivray, and Giovanni Colavizza. 2020. Assessing the Impact of OCR Quality on Downstream NLP Tasks. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ARTIDIGH, pages 484–496. INSTICC, SciTePress. [find](https://scholar.google.com/scholar?q=Daniel+van+Strien%2C+Kaspar+Beelen%2C+Mariona+Ardanuy%2C+Kasra+Hosseini%2C+Barbara+McGillivray%2C+and+Giovanni+Colavizza%2E+2020%2E+Assessing+the+Impact+of+OCR+Quality+on+Downstream+NLP+Tasks%2E+In+Proceedings+of+the)
6. Roberta Killick, Paul Fearnhead, and Idris A. Eckley. 2012. Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500):1590–1598. [↗](https://doi.org/10.1080/01621459.2012.737745)
7. M. Kytö, M. Rydén, and E. Smitterberg, editors. 2006. Nineteenth-century English: Stability and change. Cambridge University Press, Cambridge. [find](https://scholar.google.com/scholar?q=M%2E+Kyto%CC%88%2C+M%2E+Ryde%CC%81n%2C+and+E%2E+Smitterberg%2C+editors%2E+2006%2E+Nineteenth%2Dcentury+English%3A+Stability+and+change%2E+Cambridge+University+Press%2C+Cambridge%2E)
8. Melvin Wevers and Marijn Koolen. 2020. Digital begriffsgeschichte: Tracing semantic change using word embeddings. Historical Methods: A Journal of Quantitative and Interdisciplinary History, 53(4):226–243. [↗](https://doi.org/10.1080/01615440.2020.1760157)
9. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. [Efficient estimation of word representations in vector space](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/). [↗](https://doi.org/10.48550/arxiv.1301.3781)
10. A Lynda Mugglestone. 2008. The Oxford History of English. Oxford University Press, Oxford. Scripts and models All the Python scripts used to train the diachronic word embeddings, as well as several Jupyter notebooks to replicate the methodology employed in this paper, can be found at Radim Řehůřek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45– 50, Valletta, Malta. ELRA. http://is.muni.cz/ publication/884893/en . https://github.com/Living-with-machines/DiachronicEmb-BigHistData . The vectors used for the analysis in this paper can be found in the following repository in Zenodo: https://doi.org/10.5281/zenodo.7181681 . [↗](https://doi.org/10.5281/zenodo.7181681)
11. Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi. 2020. [SemEval-2020 task 1: Unsupervised lexical semantic change detection](https://md.research.elamur.ai/free/en/papers/schlechtweg-2020-semeval-2020-task-1-unsupervised/). In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1–23, Barcelona (online). International Committee for Computational Linguistics. [↗](https://doi.org/10.18653/v1/2020.semeval-1.1)

*Updated 1 November 2026*