Graph convolutional networks for exploring authorship hypotheses
What it is about
The paper casts the Documentary Hypothesis, the claim that the Torah combines several named sources, as a span classification task. The data is the Westminster Leningrad Codex (vowel points removed), with 378 labelled spans across eight source labels. To avoid topic overfitting, the bag-of-morph features keep only the morphs that occur in every source, which cuts 6,625 morph types to 70 (about half function morphs, a fifth verbs, a fifth common nouns). Graph convolutional networks, which see each span's neighbours (a sibling relation between spans that share a parent), beat logistic regression and MLPs on macro F-score; the best row of its Table 2 is about 48.6. Granularity indicators help the models with hidden layers but hurt when the full vocabulary is used. The error analysis notes that a shared word such as "wife" can push a span to the wrong source.
Ideas
- I1. A vocabulary filter for stylistic tests: keep only the morphs that appear in every class before any Torah style measure. This blocks a topic from identifying a source.
- I2. Granularity (book, chapter, verse, word) as a feature has to be tested with and without the topic features, twice, because the paper shows it can help in one setting and do harm in another.
- I3. The sibling relation between spans can be an explicit, deterministic edge in the world store: are neighbouring spans from the same source? This is a graph query, not a neural model.
For Mova
I1 is a filter for stylistic tests in tools/macula and a guide for the function-word tables in languages/hbo/05-pronouns-particles.md. I2 is a measurement rule for any feature test. I3 belongs to world (relations between units) and to languages/hbo/09-discourse.md for structure across a text.
Institutions
United StatesCite
Cite as Tom Lippincott. 2019. Graph convolutional networks for exploring authorship hypotheses. Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature.
@inproceedings{lippincott2019graph,
title = {{Graph convolutional networks for exploring authorship hypotheses}},
author = {Tom Lippincott},
booktitle = {Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
year = {2019},
doi = {10.18653/v1/w19-2510},
url = {https://doi.org/10.18653/v1/w19-2510}
}References
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