[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/lingbulk-2019-lippincott-graph-convolutional-networks-exploring-authorship/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/) / Graph convolutional networks for exploring authorship hypotheses # Graph convolutional networks for exploring authorship hypotheses **Authors:** [Tom Lippincott](https://md.mova.elamur.ai/free/en/docs/papers/authors/tom-lippincott/) · **Year:** 2019 · **Venue:** Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature **Link:** [aclanthology.org/W19-2510](https://aclanthology.org/W19-2510/) · library `lingbulk-2019-lippincott-graph-convolutional-networks-exploring-authorship` · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) [**1** citations](https://openalex.org/works?filter=cites:W2954262374) openalex · 2026-10-18[Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature](https://aclanthology.org/volumes/W19-25/)[licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/W19-2510/) - [PDF](https://aclanthology.org/W19-2510.pdf) - [DOI 10.18653/v1/w19-2510](https://doi.org/10.18653/v1/w19-2510) - [OpenAlex](https://openalex.org/W2954262374) **Authors (1)** 1. [Lippincott, Tom](https://openalex.org/A5046184426) h 4 · 37 citations · 32 works Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart. ## 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 [Pronouns, particles and prepositions](https://md.mova.elamur.ai/free/en/docs/languages/hbo/05-pronouns-particles/). I2 is a measurement rule for any feature test. I3 belongs to `world` (relations between units) and to [Discourse: narrative, speech and the shape of a text](https://md.mova.elamur.ai/free/en/docs/languages/hbo/09-discourse/) for structure across a text. ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/w19-2510](https://doi.org/10.18653/v1/w19-2510) ``` @inproceedings{lippincott2019graph, title = {{Graph convolutional networks for exploring authorship hypotheses}}, author = {Lippincott, Tom}, 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} } ``` - [← previous Creating Linked Data Morphological Language Resources with MMoOn - The Hebrew Morpheme Inventory](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2016-klimek-creating-linked-data-morphological-language/) - [next → HELFI: a Hebrew-Greek-Finnish Parallel Bible Corpus with Cross-Lingual Morpheme Alignment](https://md.mova.elamur.ai/free/en/docs/papers/lingbulk-2020-jyra-helfi-hebrew-greek-finnish-parallel/) - [↑ back to Ancient Hebrew](https://md.mova.elamur.ai/free/en/docs/papers/hbo/) ## References 10 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Adam Amram, Anat Ben-David, and Reut Tsarfaty. 2018. Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2242–2252. [find](https://scholar.google.com/scholar?q=Adam+Amram%2C+Anat+Ben%2DDavid%2C+and+Reut+Tsarfaty%2E+2018%2E+Representations+and+Architectures+in+Neural+Sentiment+Analysis+for+Morphologically+Rich+Languages%3A+A+Case+Study+from+Modern+Hebrew%2E+In+Proceedings+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/coling-2018-amram-representations-architectures-neural-sentiment-analysis/) 2. Association for Literary and Linguistic Computing. 1977. Oxford Archive of Electronic Literature. [find](https://scholar.google.com/scholar?q=Association+for+Literary+and+Linguistic+Computing%2E+1977%2E+Oxford+Archive+of+Electronic+Literature%2E) 3. David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent Dirichlet allocation. Journal of machine Learning research, 3(Jan):993–1022. [find](https://scholar.google.com/scholar?q=David+M+Blei%2C+Andrew+Y+Ng%2C+and+Michael+I+Jordan%2E+2003%2E+Latent+Dirichlet+allocation%2E+Journal+of+machine+Learning+research%2C+3%28Jan%29%3A993%E2%80%931022%2E) 4. TEI Consortium editors. 2019. TEI P5: Guidelines for Electronic Text Encoding and Interchange. [find](https://scholar.google.com/scholar?q=TEI+Consortium+editors%2E+2019%2E+TEI+P5%3A+Guidelines+for+Electronic+Text+Encoding+and+Interchange%2E) 5. Richard Elliott Friedman. 1987. Who Wrote the Bible? Simon and Schuster. [find](https://scholar.google.com/scholar?q=Richard+Elliott+Friedman%2E+1987%2E+Who+Wrote+the+Bible%3F+Simon+and+Schuster%2E) 6. Richard Elliott Friedman. 2003. The Bible with Sources Revealed. HarperCollins. [find](https://scholar.google.com/scholar?q=Richard+Elliott+Friedman%2E+2003%2E+The+Bible+with+Sources+Revealed%2E+HarperCollins%2E) 7. Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [↗](https://arxiv.org/abs/1609.02907) 8. John Boaz Lee, Ryan A Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao. 2018. Higher-order graph convolutional networks. arXiv preprint arXiv:1809.07697. [↗](https://arxiv.org/abs/1809.07697) 9. Thomas Lippincott. 2009. A Framework for Multilayered Boundary Detection. [find](https://scholar.google.com/scholar?q=Thomas+Lippincott%2E+2009%2E+A+Framework+for+Multilayered+Boundary+Detection%2E) 10. Digital Humanities 2009. [find](https://scholar.google.com/scholar?q=Digital+Humanities+2009%2E)