---
license: cc
language:
- it
pretty_name: SubCat
---


# SubCat: A Dataset of Subordinate Categories in Human Mind and LLMs for the Italian Language
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A psycholinguistic italian dataset released with the paper <a href="https://arxiv.org/abs/2505.21301">How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian</a>. It contains a list of subordiante categories, or exemplars, for 187 concrete words or, basic-level categories.

## Dataset Creation

The dataset was created to study how Italian L1 speakers generate exemplars for common object categories. The stimuli consisted of 187 basic-level concrete categories (e.g., dog, table) organized under 12 superordinate semantic categories (e.g., animals, furniture).

An exemplar generation task was administered to 365 Italian L1 speakers. Participants were presented with a list of 15-16 categories and asked to produce as many exemplars as possible for each concept at their own pace.


The final human dataset, after cleaning and standardization, consists of 24,659 exemplars.

## Data Processing

Raw data underwent a post-processing step to correct common typos and misspellings. This was done to ensure the consistency and accuracy of the final dataset. The corrected exemplars were then standardized to a common format.


## Dataset Description

- **Curated by:** [ABSTRACTION-ERC Team](https://site.unibo.it/abstraction/it)
- **Curated by:** [AI4Text Group](https://hlt-isti.github.io/)
- **Language(s) (NLP):** Italian 
- **License:** CC BY 4.0

## Dataset Structure

The dataset contains the aggregated results of the human experiment. For row in the dataset contains a unique subordinate exemplars and related statistics. The dataset contains the following columns:

1. `category`: the super-ordinate category
2. `concept`: the basic-level category
3. `exemplar`: the generated/produced sub-ordinate level exemplar/concept
4. `exemplar_string`: a sanitized version of the exemplar
5. `availability`: a metric which represents how readily the exemplar is produced as a member of its associated category
6. `count`: the number of occurrences of the exemplar across participants
7. `min_rank`: the minimum rank of exemplar's occurrence
8. `max_rank`: the highest rank of exemplar's occurrence
9. `mean_rank`: the average rank of exemplar's occurrence
10. `first_occur`: the ratio of exemplar occurring at first rank, divided by the total number of exemplar's occurrence
11. `dominance`: the proportion of participants who produce the exemplar given its associated category
12. `abs_freq_corpus`: only for LLM's generated exemplars, the number of exemplar's occurrences in the italian corpus `ItTenTen`


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you find this dataset is useful in your own work, please consider citing it as follows:

```
@inproceedings{pedrotti-etal-2025-humans,
    title = "How Humans and {LLM}s Organize Conceptual Knowledge: Exploring Subordinate Categories in {I}talian",
    author = "Pedrotti, Andrea  and
      Rambelli, Giulia  and
      Villani, Caterina  and
      Bolognesi, Marianna",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.224/",
    doi = "10.18653/v1/2025.acl-long.224",
    pages = "4464--4482",
    ISBN = "979-8-89176-251-0",
}
```