README.md

---
language:
- it
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- feature-extraction
- text-generation
pretty_name: Dataset Metafore
configs:
- config_name: analisi
  data_files: analysis/*.csv
- config_name: analysis/min_1b_logprobs
  data_files:
  - split: train
    path: analysis/min_1b_logprobs/train-*
- config_name: analysis/min_3b_logprobs
  data_files:
  - split: train
    path: analysis/min_3b_logprobs/train-*
- config_name: clustering
  data_files: clustering/*.csv
- config_name: default
  data_files:
  - metafore/*.csv
  - results/interpretazioni_esperimento.csv
- config_name: metafore
  data_files: data/metafore.csv
- config_name: partecipanti
  data_files: data/partecipanti_dati_anagrafici.csv
- config_name: risultati
  data_files: results/*.csv
tags:
- metaphor
- psychology
- italian
- linguistics
dataset_info:
- config_name: analysis/min_1b_logprobs
  features:
  - name: m_id
    dtype: string
  - name: metafora
    dtype: string
  - name: interpretazione
    dtype: string
  - name: model_score
    dtype: float64
  - name: probability_normalized
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 541701
    num_examples: 2540
  download_size: 120341
  dataset_size: 541701
- config_name: analysis/min_3b_logprobs
  features:
  - name: m_id
    dtype: string
  - name: metafora
    dtype: string
  - name: interpretazione
    dtype: string
  - name: model_score
    dtype: float64
  - name: probability_normalized
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 541701
    num_examples: 2540
  download_size: 120370
  dataset_size: 541701
---

# 📊 Dataset Metafore

**Dataset title:** Dataset Metafore  
**Related paper:** [*Language Models and the Magic of Metaphor: A Comparative Evaluation with Human Judgments*](https://aclanthology.org/2025.clicit-1.68/) — S. Mazzoli, A. Suozzi, G. E. Lebani (CLiC-it 2025).

> This repository contains CSV files used in the study described above. The data were collected from human participants who provided metaphor interpretations and ratings, and from several Italian-trained language models whose normalized log-probabilities were computed on human interpretations and systematically constructed distractors.

---

## 🗂 Overview

The dataset contains 140 metaphors extracted from Italian parliamentary transcripts ([Chamber of Deputies, 2008–2022](https://www.camera.it/leg18/221)). Human participants provided free-form interpretations for each metaphor and rated (1–5) the **conventionality** of the expression and the **adequacy of the sentence context**. For each metaphor there are two distractors created programmatically: a *Literal Distractor (LD)* and an *Opposite Metaphorical Distractor (OMD)*. Several autoregressive LLMs (GePpeTto, Minerva family, LLaMAntino) were evaluated by computing log-likelihoods for each human interpretation and distractor; normalized log probabilities and ranks are provided.

Refer to the paper for full methodology and analyses.

---

## Organized variable list (by file)

### `dataset-metafore.csv` — master / metadata for each metaphor

Description: one row per metaphor (n = 140). This file contains the main experimental items, their balancing information, prompts shown to participants, and aggregated human judgment statistics.

| Column | Type | Description |
|---|---:|---|
| `questionario` | string | Questionnaire identifier in which the item appeared (e.g., `Q1`–`Q10`) |
| `m_id` | string | Metaphor identifier (e.g., `M1`, `M42`) |
| `gruppo_bilanciamento` | string | Balancing group (`G1`–`G7`) corresponding to the syntactic pattern distribution |
| `item` | string | Full sentence containing the metaphor |
| `v_term` | string | Lexical item annotated as metaphorical |
| `pattern` | string | Syntactic pattern of the metaphor (e.g., `N₁ di N₂`, `V ∼ N`) |
| `valenza` | string | Valency of the metaphorical element (`nessuno`, `intransitiva`, `transitiva`) |
| `classe_lessicale` | string | Part of speech of the metaphorical element (e.g., `N₁`, `Verbo`, `Aggettivo`) |
| `prompt` | string | Sentence frame shown to participants for interpretation elicitation |
| `avg_conv` | numeric | Mean conventionality rating (1–5) |
| `avg_cntxt` | numeric | Mean context adequacy rating (1–5) |
| `entropia di shannon` | numeric | Shannon entropy of interpretation distribution (measure of variability) |
| `consenso_max` | numeric | Maximum consensus score (relative frequency of most common interpretation) |
| `n_int` | integer | Number of human interpretations collected for the item |

---

#### Balanced syntactic groups (dataset)

| Pattern                     | Valency      | Metaphorical element (PoS)     | Group size (n = 140) |
|----------------------------:|:------------:|:------------------------------:|:--------------------:|
| `N₁ di N₂`                  | None         | Noun₁                          | 20                   |
| `N ∼ Adj`                   | None         | Noun                           | 20                   |
| `N ∼ Adj`                   | None         | Adjective                      | 20                   |
| `N₁ = N₂`                   | None         | Noun₂                          | 20                   |
| `V ∼ N` *(intransitive)*    | Intransitive | Verb                           | 20                   |
| `V ∼ N` *(transitive)*      | Transitive   | Verb                           | 20                   |
| `V ∼ N` *(transitive)*      | Transitive   | Verb **and** Noun              | 20                   |

---

## ❝ Cite

```
@inproceedings{mazzoli-etal-2025-language,
    title = "Language Models and the Magic of Metaphor: A Comparative Evaluation with Human Judgments",
    author = "Mazzoli, Simone  and
      Suozzi, Alice  and
      Lebani, Gianluca",
    editor = "Bosco, Cristina  and
      Jezek, Elisabetta  and
      Polignano, Marco  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025)",
    month = sep,
    year = "2025",
    address = "Cagliari, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://aclanthology.org/2025.clicit-1.68/",
    pages = "710--721",
    ISBN = "979-12-243-0587-3"
}
```

---