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# Unsupervised Decomposition of a Document into Authorial Components

**Authors:** [Moshe Koppel](https://md.mova.elamur.ai/free/en/docs/papers/authors/moshe-koppel/); Navot Akiva; Idan Dershowitz; [Nachum Dershowitz](https://md.mova.elamur.ai/free/en/docs/papers/authors/nachum-dershowitz/) · **Year:** 2011 · **Venue:** Proceedings of the 49th Annual Meeting of the ACL: Human Language Technologies, pp. 1356–1364 **Link:** [aclanthology.org/P11-1136](https://aclanthology.org/P11-1136/) · library `aclbulk-2011-koppel-unsupervised-decomposition-document-into-authorial` · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**35** citations](https://openalex.org/works?filter=cites:W2123529824) openalex-snapshot · 2026-10-14[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2011-koppel-unsupervised-decomposition-document-into-authorial/) of the 140673 papers whose bibliographies we read[Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/P11-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P11-1136/)
- [PDF](https://aclanthology.org/P11-1136.pdf)
- [OpenAlex](https://openalex.org/W2123529824)

**Authors (4)**

1. [Moshe Koppel](https://openalex.org/A5019908505) [Semantic Scholar](https://www.semanticscholar.org/author/145200175) · h 37 · 6949 citations · 150 works
2. [Navot Akiva](https://openalex.org/A5000836036) [Semantic Scholar](https://www.semanticscholar.org/author/3332806) · h 10 · 313 citations · 24 works
3. [Idan Dershowitz](https://openalex.org/A5086305897) [ORCID](https://orcid.org/0000-0002-5310-8504) · [Semantic Scholar](https://www.semanticscholar.org/author/1923479) · h 6 · 161 citations · 21 works
4. [Nachum Dershowitz](https://openalex.org/A5039993352) [ORCID](https://orcid.org/0000-0003-0363-2735) · [Semantic Scholar](https://www.semanticscholar.org/author/1759551) · h 43 · 8852 citations · 511 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

The question is whether the authorial layers of a composite text can be separated without any labelled example. The test bed is the Hebrew Bible. Two books (Jeremiah and Ezekiel, and later Isaiah, Job and Proverbs) are "munged" into one artificial document, and the method must recover the two books. Clustering chapters by bag-of-words gives a normalised majority diagonal (NMD) of 51.5%, close to the chance floor of 50%. Clustering by synonym choice, where synsets come from words that the King James translation renders identically, gives 91 of 100 chapters on the right side. A two-stage method uses the reliable core of those clusters as training data for an SVM on generic words, and reaches 99 of 100. On the munged pairs most verses are assigned correctly, and the split agrees with the Priestly and non-Priestly consensus for over 90% of Pentateuch verses where a consensus exists.

## Ideas

- I1. Synonym choice without a thesaurus: a synset is a set of source words that one translation renders with the same English word, with the concordance giving the senses. For Mova, the lemmas and Strong's numbers in `macula dict` could give Hebrew synsets, and the test is whether synonym choice separates the layers of the Pentateuch in [The lexicon](https://md.mova.elamur.ai/free/en/docs/languages/hbo/10-lexicon/).
- I2. A two-stage design as a general method: a high-precision partial clustering from a sparse, rule-like feature set, then a small classifier on a denser feature set trained only on the cores. This fits the rule-first design of Mova, since the sparse stage can be a set of rules and the dense stage a small model that can be inspected.
- I3. A negative control: munge two books of known authorship and check that the method recovers them, with the naive 51.5% as the floor that must be beaten. A test that cannot show failure is not a gate; the munged set is for tests only and is not part of any release.

## For Mova

I1 goes into [The lexicon](https://md.mova.elamur.ai/free/en/docs/languages/hbo/10-lexicon/) and `tools/macula`. I2 is a design note for `world`, where clusters of text can be built on a rule-first basis. I3 is a test under `tools/hbo` or `tools/macula`, with a dated run.

## Cite

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

```
@inproceedings{koppel2011unsupervised,
  title = {{Unsupervised Decomposition of a Document into Authorial Components}},
  author = {Koppel, Moshe and Akiva, Navot and Dershowitz, Idan and Dershowitz, Nachum},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1136/}
}
```

- [← previous How to Establish a Verbal Paradigm on the Basis of Ancient Syriac Manuscripts](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2009-peursen-how-establish-verbal-paradigm-basis/)
- [next → Stylometry in Computer-Assisted Translation: Experiments on the Babylonian Talmud](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2017-giovannetti-stylometry-computer-assisted-translation-experiments/)
- [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/)

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