--- title: "The Frequency Distribution of English Syntagms — Beebe 1973" description: "The author presents this as a pilot study that shows syntactic profiles can serve as a genre marker. 1 reference, cited by 1 paper here." authors: ["Ralph D. Beebe"] year: 1973 venue: "COLING 1973 Volume 2: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics" source: "https://aclanthology.org/C73-2002/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/beebe-1973-frequency-distribution-english-syntagms/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/beebe-1973-frequency-distribution-english-syntagms/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [1973](https://md.research.elamur.ai/free/en/papers/1973/) / The Frequency Distribution of English Syntagms # The Frequency Distribution of English Syntagms **Authors:** Ralph D. Beebe · **Year:** 1973 · **Venue:** [COLING 1973 Volume 2: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 1973](https://md.research.elamur.ai/free/en/venues/coling/1973/)) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/C73-2002/) · [PDF](https://aclanthology.org/C73-2002.pdf) · [OpenAlex](https://openalex.org/W1969468339) **Citations:** 1 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read ## Summary Beebe asks whether the frequency of syntactic constructions, rather than vocabulary, can tell apart subsets of English such as genres. The practical motive was remedial English teaching: courses for a given genre should stress the structures that genre uses most. Sentences are hand-parsed with a simple dependency grammar that non-programmers can apply. Each tree is written as a linear string using a notation adapted from Wiswesser's chemical line formulas. A program turns each string into node level numbers, cuts out every subtree (called a "syntagm"), and stores them in a matrix grouped by head category, with counts, depth, length and type/token ratios. Chi-square tests then check whether batches within a genre are homogeneous and whether genres differ. Batches of newspaper editorials were mostly homogeneous. Higher School Certificate essays were also homogeneous, but they differed clearly from editorials. Per-sentence counts of prepositional-phrase syntagms fit a Poisson distribution well. The author presents this as a pilot study that shows syntactic profiles can serve as a genre marker. ## Key points - **Notation:** the "Beebe line notation" walks each sentence group (subject, verb, object) along its leftmost path first. It writes a dash for each step back to a branching point and puts boundary markers between groups. - **Category codes:** letters stand for categories (e.g., H = prepositional phrase, P = preposition, N = noun, T = determiner, Q = qualifier). Numeric subcodes refine them, such as L1–L5 for clause types and Q1–Q3 for adjective degree. - **Homogeneity within editorials:** ten editorial batches gave chi-square 101.88 at df 90 (not significant). Batches 1–10 against 11–20 gave 9.76 at df 10 (not significant). - **Genre contrast:** four HSC essay batches gave 27.33 at df 21 (not significant). Essays against an equal number of editorial sentences gave 26.31 at df 10, significant at the .01 level. - **Poisson fit:** the corpus had 415 sentences from twenty editorials. HP-N had a mean of about 0.636 per sentence, and its chi-square against Poisson was 1.959 at df 3. All five tested prepositional syntagms fit Poisson at the 10% level, and no syntagm appeared more than about ten times in one sentence. ## Interesting ideas - Encoding a tree as a bracket-free string plus a parallel array of depth levels lets every subtree be extracted with one linear scan: start at a node and stop when the level falls below the start node's level. - A genre is characterised by the relative frequencies of whole subtree shapes, and chi-square homogeneity is checked across batches before any genre comparison is trusted. - Each sentence's count of a construction is compared with a Poisson expectation, so deviations reveal clustering or avoidance beyond chance. - Optional arrowheads record where word order departs from a stated default order, and counting them tests how valid that default actually is. - Syntagm type/token ratios are tracked as more sentences are added, which shows when the inventory of constructions saturates. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (tree): The same depth-array trick could quickly enumerate all UD subtrees for indexing, counting or pattern matching without recursive traversal. - **I2** (learn): Subtree-shape profiles of silver-parsed text could flag domain shift or a misbehaving parser, using within-genre batch homogeneity as the negative control. - **I3** (learn): Applying this to parser outputs could show when a model over- or under-produces constructions such as stacked PPs, compared with gold treebank rates. - **I4** (back): Generation could use such measured departure rates to decide when non-canonical order, such as fronted adverbials, is natural for a given genre. - **I5** (world): Saturation curves could estimate how much text is needed before frame or construction inventories for a domain stop growing. *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 (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works 🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works ## Cite Cite as: Ralph D. Beebe. 1973. The Frequency Distribution of English Syntagms. COLING 1973 Volume 2: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{beebe1973frequency, title = {{The Frequency Distribution of English Syntagms}}, author = {Ralph D. Beebe}, booktitle = {COLING 1973 Volume 2: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics}, year = {1973}, doi = {10.3115/992567.992570}, url = {https://doi.org/10.3115/992567.992570} } ``` - [← previous Anglo-Saxon Onomastics in the Old English Andreas](https://md.research.elamur.ai/free/en/papers/robinson-1973-anglo-saxon-onomastics-old-english/) - [next → The Development of Old English Diphthongs](https://md.research.elamur.ai/free/en/papers/steponavicius-1973-development-old-english-diphthongs/) - [↑ back to papers of 1973](https://md.research.elamur.ai/free/en/papers/1973/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. W. J. Wxsw~ss~R, A Line-Formula Chemical Notation, Thomas Y. Cromwell Company, 1954. [find](https://scholar.google.com/scholar?q=W%2E+J%2E+Wxsw%7Ess%7ER%2C+A+Line%2DFormula+Chemical+Notation%2C+Thomas+Y%2E+Cromwell+Company%2C+1954%2E) *Updated 31 October 2026*