--- title: "The Clustering of Author's Texts of English Fiction in the Vector" description: "Pavlyshenko instead describes each document by how often its words fall into broad semantic fields… 8 references, cited by 2 papers here." authors: ["Bohdan Pavlyshenko"] year: 2012 venue: "arXiv:1212.1478" source: "https://arxiv.org/abs/1212.1478" doi: "10.48550/arxiv.1212.1478" arxiv: "1212.1478" licence: "arXiv — non-exclusive license to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/pavlyshenko-2012-clustering-author-s-texts-english/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/pavlyshenko-2012-clustering-author-s-texts-english/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) / The Clustering of Author's Texts of English Fiction in the Vector Space of Semantic Fields # The Clustering of Author's Texts of English Fiction in the Vector Space of Semantic Fields **Authors:** Bohdan Pavlyshenko · **Year:** 2012 · **Venue:** arXiv:1212.1478 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/1212.1478) · [PDF](https://arxiv.org/pdf/1212.1478) · [DOI 10.48550/arxiv.1212.1478](https://doi.org/10.48550/arxiv.1212.1478) · [arXiv 1212.1478](https://arxiv.org/abs/1212.1478) **Citations:** 2 in our library, of the 141,840 papers whose bibliographies we read ## Summary Clustering documents by raw word frequencies produces vectors as large as the vocabulary, which makes the computation heavy. Pavlyshenko instead describes each document by how often its words fall into broad semantic fields, which are word groups that share a general concept such as motion or perception. The fields come from WordNet's lexicographer files for nouns and verbs. Each field's weight is the summed frequency of its member words, normalized so that every document's profile adds up to one. The study clusters 503 English fiction works by 17 authors hierarchically, using Ward's method and Euclidean distance, and stops at 20 clusters. A second run first projects the field-by-document matrix onto its leading singular vectors (SVD, as in LSA) and then clusters again. Some clusters mix many writers, while others are dominated by one or a few. The author interprets the dominated clusters as regions of semantic space that reflect individual idiolects. This pattern persists at 10 dimensions but disappears at 3. The result suggests that a small, interpretable feature space can still carry authorial signal. ## Key points - The corpus contains 503 literary works by 17 authors of English fiction. - The features come from WordNet lexicographer files: - 26 noun files (e.g. noun.feeling, noun.artifact), from which 54,464 words were selected; - 15 verb files (e.g. verb.motion, verb.perception), from which 9,097 words were selected; - derived word forms were also counted toward their fields. - Clustering is agglomerative with Ward's criterion and Euclidean distance, halted when 20 clusters remain. Results are shown as a dendrogram and per-cluster histograms of authors. - SVD of the field-frequency matrix shows singular values falling off sharply. The first 10 singular directions form an orthogonal space of "secondary" fields. - Author-dominated clusters appear in both the full field space and the 10-dimensional SVD space, but not in a 3-dimensional one. Evaluation is visual only; no purity or accuracy figures are reported. ## Interesting ideas - WordNet supersense (lexicographer-file) distributions give a document a compact profile in which every dimension has a human-readable name, unlike raw vocabulary vectors or opaque embeddings. - Keeping noun fields and verb fields as separate axes splits "what things a text talks about" from "what kinds of actions happen" while keeping both coarse. - Running SVD on an already small, meaningful matrix and sweeping the target rank (10 keeps the effect, 3 destroys it) locates where the signal actually lives. - Inspecting the label composition of each cluster separates regions of the space that discriminate between classes from regions that are semantically generic and uninformative. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (world): Compute a supersense profile over the entities and events already placed in a text's world, not over tokens, to give an explainable genre or topic signature that can steer which compiled knowledge to load. - **I2** (tokens): Attach the lexfile supersense to each lemma in crate en as a cheap semantic tag that later frame and role assignment can consult or check against. - **I3** (learn): Use rank or feature-ablation sweeps as an evaluation habit, treating the setting where a claimed effect vanishes as a built-in negative control. - **I4** (learn): Cluster sentences by supersense profile and overlay witness-parser disagreement rates, so repair-rule induction and LLM-teacher effort target the clusters where errors concentrate. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works 🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works ## Cite Cite as: Bohdan Pavlyshenko. 2012. The Clustering of Author's Texts of English Fiction in the Vector Space of Semantic Fields. arXiv:1212.1478. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{pavlyshenko2012clustering, title = {{The Clustering of Author's Texts of English Fiction in the Vector Space of Semantic Fields}}, author = {Bohdan Pavlyshenko}, year = {2012}, doi = {10.48550/arxiv.1212.1478}, eprint = {1212.1478}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.1212.1478} } ``` - [← previous Some General Remarks on Interactions between Aspect, Modality, and Evidentiality in Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/gzella-2012-some-general-remarks-interactions-between/) - [next → The Morphology of the tG-Stem in Hebrew and tirgaltî in Hos 11:3](https://md.research.elamur.ai/free/en/papers/hutton-2012-morphology-tg-stem-hebrew-tirgalti/) - [↑ back to papers of 2012](https://md.research.elamur.ai/free/en/papers/2012/page/2/) ## References 8 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Deerwester, S., Dumais, S.T., Furnas G. W., Landauer T. K., Harshman, R. 1990. Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41: 391–407. [find](https://scholar.google.com/scholar?q=Deerwester%2C+S%2E%2C+Dumais%2C+S%2ET%2E%2C+Furnas+G%2E+W%2E%2C+Landauer+T%2E+K%2E%2C+Harshman%2C+R%2E+1990%2E+Indexing+by+Latent+Semantic+Analysis%2E+Journal+of+the+American+Society+for+Information+Science+41%3A+391%E2%80%93407%2E) 2. Fellbaum, C. 1998. WordNet. An Electronic Lexical Database. Cambridge, MA: MIT Press. [↗](https://doi.org/10.7551/mitpress/7287.001.0001) 3. Gliozzo, Alfio, Strapparava, Carlo. 2009 . Semantic Domains in Computational Linguistics. Springer . [↗](https://doi.org/10.1007/978-3-540-68158-8) 4. Larsen, B., Aone, C. 1999. Fast and effective text mining using linear-time document clustering. [find](https://scholar.google.com/scholar?q=Larsen%2C+B%2E%2C+Aone%2C+C%2E+1999%2E+Fast+and+effective+text+mining+using+linear%2Dtime+document+clustering%2E) 5. Manning, C. D., Raghavan P., Schütze, H., 2008. Introduction to Information Retrieval. Cambridge University Press. [↗](https://doi.org/10.1017/cbo9780511809071) 6. Pantel, P., Turney, P. D., 2010. [From Frequency to Meaning: Vector Space Models of Semantics](https://md.research.elamur.ai/free/en/papers/turney-2010-frequency-meaning-vector-space-models/). Journal of Artificial Intelligence Research 37:141–188. Proceeding KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 16–22. ACM New York. [↗](https://doi.org/10.1613/jair.2934) 7. Sebastiani, F. 2002. Machine Learning in Automated Text Categorization ACM Computing Surveys. 34:1–47. [↗](https://doi.org/10.1145/505282.505283) 8. Shehata, S. Karray, F., Kamel, M. 2006. Enhancing Text Clustering Using Concept-based Mining Model. Data Mining, ICDM '06. Sixth International Conference, pp.1043–1048. Waterloo. [find](https://scholar.google.com/scholar?q=Shehata%2C+S%2E+Karray%2C+F%2E%2C+Kamel%2C+M%2E+2006%2E+Enhancing+Text+Clustering+Using+Concept%2Dbased+Mining+Model%2E+Data+Mining%2C+ICDM+%2706%2E+Sixth+International+Conference%2C+pp%2E1043%E2%80%931048%2E+Waterloo%2E) *Updated 24 October 2026*