---
license: cc-by-sa-4.0
language:
- pt
task_categories:
- token-classification
tags:
- portuguese
- european-portuguese
- homographs
- word-sense-disambiguation
- grapheme-to-phoneme
- text-to-speech
- ipa
- out-of-distribution
pretty_name: bifonia — Portuguese Heterophonic Homographs (Wild / OOD)
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: test.jsonl
---

# bifonia-pt-homographs-wild — real-world OOD evaluation set

Real Portuguese sentences containing **112 heterophonic homographs** — words spelled
identically whose pronunciation (IPA) depends on **meaning**, e.g. `sede`
(thirst `ˈsedɨ` vs seat `ˈsɛdɨ`, both nouns), `forma` (mould `ˈfoɾmɐ` vs shape
`ˈfɔɾmɐ`), `molho` (sauce `ˈmoʎu` vs bundle `ˈmɔʎu`).

Unlike the synthetic [`TigreGotico/bifonia-pt-homographs`](https://huggingface.co/datasets/TigreGotico/bifonia-pt-homographs)
train/test set (LLM-generated), every sentence here is **mined from real-world text**.
It is meant as an **out-of-distribution (OOD) test set** — a fair probe of how well a
disambiguator generalises beyond synthetic phrasing, for grapheme-to-phoneme / TTS
front-ends and word-sense disambiguation.

## Schema

| field | description |
|-------|-------------|
| `word` | the ambiguous headword |
| `sense` | the **meaning** slug that selects the pronunciation (e.g. `thirst` / `seat`) |
| `pos` | descriptive part of speech of this reading |
| `ipa` | European-Portuguese transcription of `word` in this reading |
| `sentence` | the real sentence containing `word` in that meaning |

7,119 sentences, 112 words. The roster was expanded beyond the original 27 with noun/verb and lexical heterophone pairs; the new words were mined the same way (Portuguese Wikipedia) and sense-labelled with Claude Sonnet. Real-world frequency is skewed — common senses dominate and several rare words occur only a few times or not at all. Real-world frequency is skewed:
common senses dominate and several minority senses (e.g. the nautical `sobre`=sail)
do not occur at all — which is itself useful signal.

## Sources & licensing

Sentences are drawn from:
- **Portuguese Wikipedia** (CC-BY-SA-4.0) — via the `wikimedia/wikipedia` dump.
- **CommonCrawl** — via the `cc100` (pt) corpus.

The dataset is released under **CC-BY-SA-4.0** to honour Wikipedia's share-alike terms.
Attribute Wikipedia and CommonCrawl when redistributing. Text is mixed European and
Brazilian Portuguese; the **meaning labels are dialect-neutral**, while the `ipa` column
follows European Portuguese (pt-PT).

## Annotation

Each sentence's `sense` was labelled by **Claude (Sonnet)**, shown the word's exact
sense inventory and asked to choose one meaning or mark it unclear; unclear/invalid
labels were dropped. Labels are model-assigned and may contain noise, especially for
rare words (e.g. `tola` n=19, `rego` n=28, `conserto` n=62).

## Benchmark (sense prediction on this OOD set)

| approach | OOD accuracy |
|---|---|
| most-common (majority sense per word) | 47.5% |
| rule-based (knowledge, no training) | 83.2% |
| Naive-Bayes (corpus-trained) | 86.7% |
| averaged perceptron (corpus-trained) | **89.6%** |

Every approach drops vs the synthetic test set (where the same systems score 94–99%),
confirming that synthetic benchmarks overstate accuracy. A corpus-trained perceptron
still beats the knowledge-based rules by ~6 points on real text.

## Words

`acordo`, `acerto`, `cerro`, `choro`, `colher`, `começo`, `conserto`, `coro`,
`corte`, `forma`, `gosto`, `gozo`, `jogo`, `molho`, `olho`, `para`, `pelo`,
`peso`, `porto`, `posto`, `rego`, `seco`, `sede`, `sobre`, `tola`, `torre`,
`transtorno`.

## Citation

```bibtex
@misc{bifonia_wild_2025,
  title  = {bifonia-pt-homographs-wild: a real-world OOD set for Portuguese heterophone disambiguation},
  author = {JarbasAI / TigreGotico},
  year   = {2025},
  url    = {https://github.com/TigreGotico/bifonia},
  license = {CC-BY-SA-4.0},
}
```