---
title: "Grammar and Meaning — paper by Bizzoni et al. (2019)"
description: "Most work with diachronic word embeddings tracks how the meanings of content words shift. 33 references."
authors: ["Yuri Bizzoni", "Stefania Degaetano-Ortlieb", "Katrin Menzel", "Pauline Krielke", "Elke Teich"]
year: 2019
venue: "Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change"
source: "https://aclanthology.org/W19-4722/"
doi: "10.18653/v1/w19-4722"
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/bizzoni-2019-grammar-meaning-analysing-topology-diachronic/"
---
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# Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings

**Authors:** [Yuri Bizzoni](https://md.research.elamur.ai/free/en/authors/yuri-bizzoni/), [Stefania Degaetano-Ortlieb](https://md.research.elamur.ai/free/en/authors/stefania-degaetano-ortlieb/), [Katrin Menzel](https://md.research.elamur.ai/free/en/authors/katrin-menzel/), [Pauline Krielke](https://md.research.elamur.ai/free/en/authors/pauline-krielke/) and [Elke Teich](https://md.research.elamur.ai/free/en/authors/elke-teich/) · **Year:** 2019 · **Venue:** [Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change](https://md.research.elamur.ai/free/en/venues/lchange/) ([Proceedings for the International Workshop on Computational Approaches to Language Change 2019](https://md.research.elamur.ai/free/en/venues/lchange/2019/)) · **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/W19-4722/) · [PDF](https://aclanthology.org/W19-4722.pdf) · [DOI 10.18653/v1/w19-4722](https://doi.org/10.18653/v1/w19-4722) · [OpenAlex](https://openalex.org/W2973102651)

**Citations:** 9 citations (OpenAlex, 2026-10-14)

## Summary
Most work with diachronic word embeddings tracks how the meanings of content words shift. This paper applies the same machinery to grammar. The authors ask whether function words and poly-functional forms (such as verbal ing-forms) change their usage in ways that signal the emergence of a scientific register in Late Modern English.

The data is v4.0 of the Royal Society Corpus (RSC), which covers 1665–1869. The authors train one embedding model per decade with a word-order-aware structured skip-gram. They then examine the models through t-SNE maps that also encode frequency, plus two group-level distance measures (inner and outer distance). For ing-forms, they first explore the space by hand to form a hypothesis. They then test it with several clustering algorithms and characterise each cluster's contexts using relative entropy over part-of-speech n-grams.

Grammatical words behave differently from lexical ones. The ing-forms fall into academic, motion and change-of-state groups, and each group leans towards either gerund or participle use. This matters because embeddings can therefore trace stylistic and grammatical change, not just shifts in meaning.

## Key points
- **Corpus:** RSC v4.0 contains the Philosophical Transactions and Proceedings of the Royal Society of London. It has about 32 million tokens, around 10,000 documents and 966,469 sentences, and is annotated with lemma and POS.
- **Embedding method:** The setup follows Fankhauser and Kupietz (2017), using the structured skip-gram of Ling et al. (2015) with a [-5,5] window.
  - One model is trained per decade, starting from a model trained on the whole corpus.
  - Training uses non-lemmatized text with a vocabulary of 117,165 forms.
  - Similarity is measured by cosine distance, and the space is visualised with t-SNE.
- **Distance measures:** Inner distance (within a word group) and outer distance (group to rest of vocabulary) are used to compare lexical words, function words and poly-functional forms over time.
- **Clustering:** Affinity Propagation, DBSCAN and Minibatch KMeans were run on ing-forms. The resulting groups are academic, motion and change-of-state verbs.
- **Relative entropy over POS trigrams:** The comparison was 1850–69 vs 1660–1800. Academic and change-of-state verbs mostly appear in gerund contexts (e.g. preposition + VVG). Motion verbs mostly appear as participles after nouns. The highest score was 0.1116 for VVN.IN.VVG among change-of-state verbs.

## Interesting ideas
- An embedding objective that is sensitive to word order clusters words by grammatical form as well as by topic, so the choice of context model decides whether vectors reflect syntax or semantics.
- Summarising a word group by two numbers (how tight it is internally, and how far it sits from everything else) gives a cheap signal of specialisation or drift.
- Form a hypothesis about a sub-space by hand-inspecting near neighbours first, then run several clustering algorithms and judge them against that hypothesis rather than trusting raw output.
- Relative entropy over POS n-grams, weighted by probability in each period, pinpoints which local contexts are distinctive of one subset versus another and yields readable examples.
- A verb's semantic class correlates with whether its ing-form acts as a gerund or a participle (change-of-state and academic verbs vs motion verbs).

## For Mova

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

- **I1** (tokens): Order-aware vectors could serve as an explainable diagnostic for ambiguous VVG tokens, flagging ing-forms whose neighbours suggest a reading other than the one the tagger chose.
- **I2** (learn): Inner/outer distance per verb class or UD relation, computed over successive training rounds, could reveal when silver data pulls a category apart or isolates it suspiciously.
- **I3** (world): Verb classes induced from Mova's own parsed text could be clustered and checked against VerbNet-derived expectations, so that disagreements become visible candidates for ontology fixes.
- **I4** (tree): The same KLD comparison between gold treebank and silver or converted data could surface POS contexts that one dialect over-produces, pointing to conversion or parser errors.
- **I5** (frames): A verb-class prior on the gerund/participle reading could help decide whether an ing-form heads an event frame as a nominal argument or as a modifier, with the class cited as the explanation.

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

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- [Saarland University](https://md.research.elamur.ai/free/en/universities/saarland-university/) — University, Saarbrücken, Germany · 149 works · 18,353 citations

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

## Cite

Cite as: Yuri Bizzoni, Stefania Degaetano-Ortlieb, Katrin Menzel, Pauline Krielke and Elke Teich. 2019. Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings. Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change.

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

```bibtex
@inproceedings{bizzoni2019grammar,
  title = {{Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings}},
  author = {Yuri Bizzoni, Stefania Degaetano-Ortlieb, Katrin Menzel, Pauline Krielke and Elke Teich},
  booktitle = {Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change},
  year = {2019},
  doi = {10.18653/v1/w19-4722},
  url = {https://doi.org/10.18653/v1/w19-4722}
}
```

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## References

33 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*