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# Clause Structure Variation in Biblical Hebrew: A Quantitative Approach

**Authors:** [Martijn Naaijer](https://md.mova.elamur.ai/free/en/docs/papers/authors/martijn-naaijer/) · **Year:** 2020 · **Venue:** PhD thesis, Vrije Universiteit Amsterdam (supervisor W. van Peursen; co-supervisors J. Dyk, D. Roorda) **Link:** [research.vu.nl/en/publications/clause-structure-variation-in-biblical-hebrew-a-quantitative-appr](https://research.vu.nl/en/publications/clause-structure-variation-in-biblical-hebrew-a-quantitative-appr/) · **Licence:** free to read, no CC licence stated — summary only

[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/hbo-2020-naaijer-clause-structure-variation-biblical-hebrew/) of the 140673 papers whose bibliographies we readData Archiving and Networked Services (DANS)

- [Publisher's page](https://research.vu.nl/en/publications/clause-structure-variation-in-biblical-hebrew-a-quantitative-appr/)
- [PDF](https://research.vu.nl/files/118347451/130280.pdf)

**Authors (1)**

1. Martijn Naaijer

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

The full text is not in our library, and the repository page gives no abstract text, so this card rests on the title, the catalogue record (249 pages; keywords Biblical Hebrew, syntax, machine learning, statistics). From the title and keywords, the thesis studies how the order and make-up of clause constituents (subject, object, complements, explicit vs. pronominal elements) vary across the books of the Hebrew Bible, using the ETCBC database and statistical and machine-learning models to separate what is due to period, genre, and text tradition. Ideas below are provisional until the text is read.

## Ideas

- I1. Treat constituent order as a measured variable: for every finite clause in our Torah trees, record the order of verb, subject (nsubj) and object (obj) and whether each is a noun, a pronoun or a suffix; tabulate by book and by voice (narrator vs. character, from `macula prag`).
- I2. Fit a simple, explainable model (logistic regression or a decision table) predicting VS vs SV from a few features (verb form, clause type, subject definiteness, speech vs narration); report which features carry the weight, rather than a black-box classifier.

## For Mova

I1 is a count over the trees for [Syntax: the clause and the sentence](https://md.mova.elamur.ai/free/en/docs/languages/hbo/08-syntax/) (word order) and `12-biblical-mishnaic-modern.md` (stage profiles). I2 belongs with the `prag` measurements in `13-mova-tools.md`; it should use the narrator/character split that `tools/macula/src/prag.rs` already computes. Re-read and revise this card when the thesis PDF is in the library.

## Cite

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

```
@inproceedings{naaijer2020clause,
  title = {{Clause Structure Variation in Biblical Hebrew: A Quantitative Approach}},
  author = {Naaijer, Martijn},
  booktitle = {Data Archiving and Networked Services (DANS)},
  year = {2020},
  url = {https://research.vu.nl/en/publications/clause-structure-variation-in-biblical-hebrew-a-quantitative-appr/}
}
```

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