---
title: "ProPOSEL — paper by Brierley and Atwell (2008)"
description: "The paper presents ProPOSEL, an English lexicon that merges phonological, prosodic and… 23 references, cited by 1 paper here."
authors: ["Claire Brierley", "Eric Atwell"]
year: 2008
venue: "Coling 2008: Proceedings of the Workshop on Cognitive Aspects of the Lexicon (COGALEX 2008)"
source: "https://aclanthology.org/W08-1904/"
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/brierley-2008-proposel-human-oriented-prosody-pos/"
---
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# ProPOSEL: a human-oriented prosody and PoS English lexicon for machine-learning and NLP

**Authors:** [Claire Brierley](https://md.research.elamur.ai/free/en/authors/claire-brierley/) and [Eric Atwell](https://md.research.elamur.ai/free/en/authors/eric-atwell/) · **Year:** 2008 · **Venue:** Coling 2008: Proceedings of the Workshop on Cognitive Aspects of the Lexicon (COGALEX 2008) · **Publisher:** [International Committee on Computational Linguistics (ICCL)](https://md.research.elamur.ai/free/en/publishers/international-committee-on-computational-linguistics-iccl/) · **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/W08-1904/) · [PDF](https://aclanthology.org/W08-1904.pdf) · [OpenAlex](https://openalex.org/W2088502539)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/brierley-2008-proposel-human-oriented-prosody-pos/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper presents ProPOSEL, an English lexicon that merges phonological, prosodic and part-of-speech information into one file. Its starting point is CUVPlus, enriched with data from CELEX-2 and CMU. Earlier pronunciation dictionaries were built for speech recognition and seldom record word class. The authors wanted a resource that can be joined directly to tagged corpora and used as prior knowledge in machine learning, especially for predicting prosodic phrase breaks in text-to-speech. Each of the 104,049 entries has 15 fields, including:
- four PoS tagsets;
- two phonetic transcriptions;
- syllable count and lexical stress pattern;
- a default content/function word label;
- a consonant–vowel template.

CUVPlus packs several PoS variants into one field. The authors split these so that each (word form, C5 tag) pair becomes a unique key. Python/NLTK tools load the file as a dictionary and support filtered searches by sound, rhythm and word class. The paper reports no experiments, so the contribution is the resource itself. It shows how a plain-text dictionary can serve both NLP pipelines and people searching for words by partial form rather than meaning.

## Key points
- 104,049 entries, each with 15 pipe-separated fields. It is distributed as a text file and intended for open-source release with NLTK.
- Four tagging schemes allow linking to different corpora:
  - C5 for the BNC;
  - Penn Treebank for Treebank-3;
  - LOB for MARSEC;
  - C7 for the BNC Sampler.
- Pronunciation appears as SAM-PA and as syllabified, stressed DISC. Stress is coded with digits 0/1/2; for example, *objectivity* is 20,100.
- Stress can depend on the tag. The noun/adjective *present* is 10, while the verb is 01.
- Example searches:
  - four-syllable verbs stressed like *obliterate* returned 67 candidates;
  - other queries list all seven-syllable words or find rhymes for *corpus* using a regex over SAM-PA.

## Interesting ideas
- Splitting entries that merged several PoS readings into one record per (form, tag) pair makes every value unambiguous and lets corpus tokens match by exact tuple lookup.
- Storing several tagsets side by side in one entry turns the lexicon into a word-level crosswalk between annotation schemes.
- A coarse default content/function flag is stored as a cheap, transparent feature, explicitly open to later revision by context.
- The authors frame the lexicon as a priori knowledge injected into data-driven models trained on corpora, rather than as a replacement for them.
- The dictionary can be searched from partial form — syllable count, stress shape, rhyme — not only by spelling or meaning.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): key the `en` lexicon on (form, UPOS) so lemma, morphology and other features attach to a specific reading and the tagger's choice directly selects the right entry.
- **I2** (tree): such parallel tag columns could check and explain Penn/C5-to-UD conversions when bringing older treebanks into our UD dialect.
- **I3** (tokens): a default content/function bit per lexeme gives the parser and chunker an explainable prior that context rules can override with a recorded reason.
- **I4** (learn): dictionary-derived features could act as a fixed prior alongside silver data, with negative controls showing whether the lexicon actually adds accuracy.
- **I5** (back): during generation or paraphrase, candidate words could be filtered by stress pattern or rhyme when retelling fables in verse-like or rhythmically similar wording.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Leeds](https://md.research.elamur.ai/free/en/universities/university-of-leeds/) — University, Leeds, United Kingdom · 40 works · 1,822 citations
- [University of Greater Manchester](https://md.research.elamur.ai/free/en/universities/university-of-greater-manchester/) — University, Bolton, United Kingdom · 2 works · 12 citations

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🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works · [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · 480 works

## Cite

Cite as: Claire Brierley and Eric Atwell. 2008. ProPOSEL: a human-oriented prosody and PoS English lexicon for machine-learning and NLP. Coling 2008: Proceedings of the Workshop on Cognitive Aspects of the Lexicon (COGALEX 2008).

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

```bibtex
@inproceedings{brierley2008proposel,
  title = {{ProPOSEL: a human-oriented prosody and PoS English lexicon for machine-learning and NLP}},
  author = {Claire Brierley and Eric Atwell},
  booktitle = {Coling 2008: Proceedings of the Workshop on Cognitive Aspects of the Lexicon (COGALEX 2008)},
  year = {2008},
  doi = {10.3115/1598848.1598854},
  url = {https://doi.org/10.3115/1598848.1598854}
}
```

- [← previous MEDAR: Collaboration between European and Mediterranean Arabic Partners to Support the Development of Language Technology for Arabic](https://md.research.elamur.ai/free/en/papers/maegaard-2008-medar-collaboration-between-european-mediterranean/)
- [next → Reflexivity in English: an STAG analysis](https://md.research.elamur.ai/free/en/papers/storoshenko-2008-reflexivity-english-stag-analysis/)
- [↑ back to papers of 2008](https://md.research.elamur.ai/free/en/papers/2008/page/2/)

## References

23 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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2. Atwell, E., G. Demetriou, J. Hughes, A. Schriffin, C. Souter, S. Wilcock. 2000. A comparative evaluation of modern English corpus grammatical annotation schemes. ICAME Journal, vol. 24, pp. 7-23. [find](https://scholar.google.com/scholar?q=Atwell%2C+E%2E%2C+G%2E+Demetriou%2C+J%2E+Hughes%2C+A%2E+Schriffin%2C+C%2E+Souter%2C+S%2E+Wilcock%2E+2000%2E+A+comparative+evaluation+of+modern+English+corpus+grammatical+annotation+schemes%2E+ICAME+Journal%2C+vol%2E+24%2C+pp%2E+7%2D23%2E)
3. Atwell, E. and A. Roberts. 2006. Combinatory hybrid elementary analysis of text. In Kurimo, M, Creutz, M & Lagus, K (editors) Proceedings of the PASCAL Challenge Workshop on Unsupervised Segmentation of Words into Morphemes. Venice. [↗](https://doi.org/10.13140/2.1.3801.6964)
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*Updated 1 November 2026*