---
title: "ChiSense-12 — paper by Cabiddu et al. (2022)"
description: "Studies of how young children resolve word ambiguity in everyday speech have been limited because no sense-annotated corpus of…"
authors: ["Francesco Cabiddu", "Lewis Bott", "Gary Jones", "Chiara Gambi"]
year: 2022
venue: "Proceedings of the Thirteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2022.lrec-1.557/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cabiddu-2022-chisense-12-english-sense-annotated/"
---
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# ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus

**Authors:** Francesco Cabiddu, Lewis Bott, Gary Jones and Chiara Gambi · **Year:** 2022 · **Venue:** [Proceedings of the Thirteenth Language Resources and Evaluation Conference](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2022](https://md.research.elamur.ai/free/en/venues/lrec/2022/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.lrec-1.557/) · [PDF](https://aclanthology.org/2022.lrec-1.557.pdf) · [OpenAlex](https://openalex.org/W7160335392)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Studies of how young children resolve word ambiguity in everyday speech have been limited because no sense-annotated corpus of child-directed speech existed with the needed detail. The authors built ChiSense-12 from CHILDES (version 2020.1), using 53 American and British English corpora of speech addressed to 958 children aged up to 59 months. They selected 12 ambiguous words, each with a frequent "dominant" sense and a rarer "subordinate" sense, and chose senses that earlier experiments showed preschoolers already understand. Every occurrence was annotated, with no downsampling. Each token received a sense label, and when the word served as a verb's object, the verb stem was also recorded. The final resource has 15,581 utterances. Because it captures which verbs combine with which senses, it supports separating two explanations for how children disambiguate: surface co-occurrence statistics and the semantic constraints verbs place on their objects (verb-event structure). The authors report preliminary experiments built on corpus statistics and plan to train Transformer-based models on the corpus. The corpus is freely available on GitLab.

## Key points
- Source data: 72 British and American English corpora were downloaded from CHILDES through the R package childesr. The 53 corpora with target children up to 4 years (59 months) were kept, covering 958 children.
- Of 21,342 raw utterances containing the target words, 15,581 were retained (115,272 word tokens, 4,805 word types). On average the dominant sense accounts for 73% of uses (SD 13%).
- Eleven of the 12 words come from Rabagliati et al. (2013). The homophone pair flower/flour was added, and it is heavily skewed (3521 vs 350 uses, 91% dominant). Glasses is nearly balanced (683/620, 52%).
- The first author annotated each utterance with its sense and, for object uses, the preceding verb stem. Unclear cases were checked against neighbouring utterances and discarded if still unresolved. A second annotator validated 45 test conversations after training on 5.
- The authors contrast their work with Meylan et al. (2021), who used WordNet senses for CDI words and tagged only 50 tokens per 3-month interval for frequent words. That sampling thins out verb statistics for rare senses.

## Interesting ideas
- Annotating every token instead of a capped sample per frequent word keeps the co-occurrence profiles of rare senses intact. Capping quietly distorts exactly the minority cases you most want to study.
- Each noun sense is stored together with the verb that takes it as an object. This turns a sense corpus into a resource of selectional preferences ("meet" favours a person reading of "star").
- Test items are built from corpus statistics so that raw word association and verb plausibility point in opposite directions. The night/knight story is an example where the topic words favour the wrong reading.
- The sense inventory is limited to readings the target audience is known to understand, using a coarse dominant/subordinate split instead of a fine-grained adult dictionary.
- Annotators escalate to the surrounding discourse when a sentence alone is ambiguous, and drop the item if it stays unclear. Validators also get a third "something else" option, so they are never forced into a binary choice.

## For Mova

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

- **I1** (learn): When building silver data for sense or role statistics, avoid uniform per-lemma caps so that low-frequency readings keep reliable verb and argument distributions.
- **I2** (frames): Use the UD obj relation plus VerbNet selectional restrictions to choose a noun's sense from its governing verb. The trace can show which verb constraint forced the choice.
- **I3** (learn): Add negative-control items where bag-of-words association suggests the wrong answer, so evaluation confirms the structural path is actually doing the work.
- **I4** (answer): For fables, tales and school tests, restrict ambiguous nouns to a small, audience-appropriate sense set. This reduces spurious WordNet distinctions that confuse the reader.
- **I5** (world): Resolve entity senses by widening the window to earlier sentences, and record an explicit "unresolved/other" state instead of committing to a guess.

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

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- [Cardiff University](https://md.research.elamur.ai/free/en/universities/cardiff-university/) — University, Cardiff, United Kingdom · 23 works · 2,549 citations
- [Nottingham Trent University](https://md.research.elamur.ai/free/en/universities/nottingham-trent-university/) — University, Nottingham, United Kingdom · 2 works · 258 citations

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## Cite

Cite as: Francesco Cabiddu, Lewis Bott, Gary Jones and Chiara Gambi. 2022. ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus. Proceedings of the Thirteenth Language Resources and Evaluation Conference.

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

```bibtex
@inproceedings{cabiddu2022chisense,
  title = {{ChiSense-12: An English Sense-Annotated Child-Directed Speech Corpus}},
  author = {Francesco Cabiddu, Lewis Bott, Gary Jones and Chiara Gambi},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/2phyyubdtp5e},
  url = {https://doi.org/10.63317/2phyyubdtp5e}
}
```

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