Datasets:

lsadouk1111
/

QESC

---
language:
- en
- fr
- ar
license: cc-by-nc-4.0
task_categories:
- text-retrieval
- text-classification
pretty_name: QESC - Quranic Emotional Situation Corpus
size_categories:
- n<1K
tags:
- Islamic education
- emotional support
- children
- Arabic
- Moroccan Darija
- NLP
- retrieval
- CASS
- prophet stories
- emotional intensity
---
# QESC — Quranic Emotional Situation Corpus

## Dataset Description

**QESC** is the first structured corpus of Quranic prophet situational scenes annotated for emotional intensity and narrative resolution type, designed for child-facing emotional support retrieval in multilingual contexts (English, French, Moroccan Darija).

The corpus supports the **CASS (Context-Aware Semantic Similarity)** framework — a lightweight safety-aware retrieval system that matches a child's free-text emotional expression to the most contextually appropriate prophet story, while preventing emotionally dangerous mismatches.

- **Paper:** *CASS: A Context-Aware Semantic Similarity Framework for Safe Retrieval in Child-Facing Educational Applications* (Sadouk and Gadi)
- **GitHub:** [https://github.com/lsadouk/CASS](https://github.com/lsadouk/CASS)
- **Application:** Ya Allah — Islamic children's emotional support app

---

## Dataset Summary

| Property | Value |
|---|---|
| Total entries | 100 |
| Quranic figures covered | 26 |
| EI scale | 1–5 (mild → severe) |
| RT scale | 1–3 (immediate → delayed) |
| Primary language | English |
| Additional languages | Arabic (names), French, Moroccan Darija (paraphrases) |
| License | CC BY-NC 4.0 |

---

## Supported Tasks

- **Emotional situation retrieval** — match a child's free-text emotional query to the most appropriate prophet story
- **Emotional intensity classification** — predict EI level from situation description
- **Resolution type classification** — predict RT from situation description

---

## Dataset Structure

### Fields

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique identifier e.g. `moses_003` |
| `prophet_latin` | string | Prophet name in English e.g. `Moses` |
| `prophet_arabic` | string | Prophet name in Arabic e.g. `موسى` |
| `situation_en` | string | 3–5 sentence English description of the emotional situation |
| `emotions` | list[string] | 2–5 primary emotion labels |
| `EI` | float | Emotional Intensity 1.0–5.0 (half-point increments) |
| `RT` | int | Resolution Type: 1=immediate, 2=gradual, 3=delayed |
| `RT_label` | string | Human-readable RT label |
| `child_paraphrase` | string | Child-friendly multilingual explanation and lesson |
| `quran_ref` | string | Quranic verse or hadith reference |
| `RL` | int | Recitation Level 1–3 |
| `gender_of_protagonist` | string | `male` or `female` |
| `annotator_agreed` | bool | Annotation confirmed by domain reviewer |
| `version` | string | Corpus version |

### EI Distribution

| EI | Label | Count |
|---|---|---|
| 1 | Mild everyday emotion | 19 |
| 2 | Low-moderate distress | 19 |
| 3 | Moderate distress | 19 |
| 4 | High distress | 24 |
| 5 | Severe crisis-level distress | 19 |

### RT Distribution

| RT | Label | Count |
|---|---|---|
| 1 | Immediate resolution | 27 |
| 2 | Gradual resolution | 29 |
| 3 | Delayed resolution (long patient endurance) | 23 |

### Quranic Figures Covered

Prophets: Adam, Idris, Noah, Hud, Salih, Abraham, Lot, Ishmael, Isaac, Jacob, Joseph, Job, Shuaib, Moses, Aaron, David, Solomon, Elijah, Elisha, Jonah, Zachariah, John, Jesus, Muhammad.

Key figures (non-prophets): Mary, Hagar.

---

## Example Entry

```json
{
  "id": "joseph_003",
  "prophet_latin": "Joseph",
  "prophet_arabic": "يوسف",
  "situation_en": "Joseph was separated from his beloved father Jacob
    and thrown into a well by his own brothers out of jealousy.
    He was then sold into slavery in Egypt, far from everything
    he loved. Despite years of separation, Joseph never lost hope
    in Allah or stopped praying for his family.",
  "emotions": ["grief", "loneliness", "abandonment", "yearning", "patient_hope"],
  "EI": 3.0,
  "RT": 3,
  "RT_label": "delayed_resolution",
  "child_paraphrase": "Prophet Yusuf missed his father every single
    day for many years. He never stopped loving him even when they
    were far apart. Allah never forgot Yusuf, and one day reunited
    them in a way more beautiful than either could have imagined.",
  "quran_ref": "12:15-18",
  "RL": 2,
  "gender_of_protagonist": "male",
  "annotator_agreed": true,
  "version": "1.0"
}
```

---

## Usage

```python
from datasets import load_dataset
ds = load_dataset("lamyaa/QESC")
# All entries
print(f"Total entries: {len(ds['train'])}")
# Filter by EI level
mild = [e for e in ds['train'] if e['EI'] <= 2.0]
severe = [e for e in ds['train'] if e['EI'] == 5.0]
# Filter by prophet
joseph = [e for e in ds['train'] if e['prophet_latin'] == 'Joseph']
# Filter by resolution type
immediate = [e for e in ds['train'] if e['RT'] == 1]
```

### Use with CASS retrieval

```python
# See full code in: https://github.com/lsadouk/CASS
# English query
retrieve("I feel sad and nobody understands me")
# → joseph_003 (EI=3, RT=3)
# French query
retrieve("Je me sens abandonné par mes amis")
# → muhammad_002 (EI=3, RT=2)
# Moroccan Darija query
retrieve("ma kaynch had li yfahmni")
# → shuaib_001 (EI=3, RT=3)
```

---

## Safety Properties

QESC is designed with child safety in mind. The EI annotation enables the CASS framework to prevent emotionally dangerous matches:

- A child at EI=1 (mild embarrassment) will **never** receive an EI=5 story (extreme suffering)
- A child at EI=5 (severe crisis) will **never** receive an EI=1 story (gentle everyday lesson)
- Safety filtering is automatic — no user configuration required

Across 5 diagnostic queries, CASS suppressed **27 of 28** emotionally dangerous entries with a mean rank drop of **37 positions** and maximum of **62 positions**.

---

## Annotation Guidelines

**EI scale:**
- EI=1: Mild everyday emotion — embarrassment, small disappointment, gentle worry
- EI=2: Low-moderate distress — social exclusion, mild loneliness, minor conflict
- EI=3: Moderate distress — betrayal, sustained loneliness, public humiliation
- EI=4: High distress — acute fear, grief, unjust accusation, family conflict
- EI=5: Severe crisis — extreme suffering, hopelessness, years of loss

**RT scale:**
- RT=1: Immediate resolution — comfort or answer arrives quickly (within the same scene)
- RT=2: Gradual resolution — situation improves over weeks or months
- RT=3: Delayed resolution — resolution requires long patient endurance (years)

---

## Citation

```bibtex
@article{sadouk2025cass,
  title     = {CASS: A Context-Aware Semantic Similarity Framework
               for Safe Retrieval in Child-Facing Educational Applications},
  author    = {Sadouk, Lamyaa AND Gadi, Taoufiq},
  journal   = {Education and Information Technologies},
  year      = {2025},
  publisher = {Springer}
}
```

---

## Dataset Card Authors

**Lamyaa Sadouk**
Ecole Marocaine des Sciences de l'Ingénieur, Casablanca, Morocco

**Taoufiq Gadi**
Laboratoire de Recherche en Mathématique, Informatique et Sciences de l'Ingénieur (MISI), Morocco

## License

[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — Attribution-NonCommercial 4.0 International

You may use and share this dataset for research and educational purposes with attribution. Commercial use is not permitted without explicit permission from the author.

	---
	language:
	- en
	- fr
	- ar
	license: cc-by-nc-4.0
	task_categories:
	- text-retrieval
	- text-classification
	pretty_name: QESC - Quranic Emotional Situation Corpus
	size_categories:
	- n<1K
	tags:
	- Islamic education
	- emotional support
	- children
	- Arabic
	- Moroccan Darija
	- NLP
	- retrieval
	- CASS
	- prophet stories
	- emotional intensity
	---

	# QESC — Quranic Emotional Situation Corpus

	## Dataset Description

	QESC is the first structured corpus of Quranic prophet situational scenes annotated for emotional intensity and narrative resolution type, designed for child-facing emotional support retrieval in multilingual contexts (English, French, Moroccan Darija).

	The corpus supports the CASS (Context-Aware Semantic Similarity) framework — a lightweight safety-aware retrieval system that matches a child's free-text emotional expression to the most contextually appropriate prophet story, while preventing emotionally dangerous mismatches.

	- Paper: CASS: A Context-Aware Semantic Similarity Framework for Safe Retrieval in Child-Facing Educational Applications (Sadouk and Gadi)
	- GitHub: [https://github.com/lsadouk/CASS](https://github.com/lsadouk/CASS)
	- Application: Ya Allah — Islamic children's emotional support app

	---

	## Dataset Summary

	\| Property \| Value \|
	\|---\|---\|
	\| Total entries \| 100 \|
	\| Quranic figures covered \| 26 \|
	\| EI scale \| 1–5 (mild → severe) \|
	\| RT scale \| 1–3 (immediate → delayed) \|
	\| Primary language \| English \|
	\| Additional languages \| Arabic (names), French, Moroccan Darija (paraphrases) \|
	\| License \| CC BY-NC 4.0 \|

	---

	## Supported Tasks

	- Emotional situation retrieval — match a child's free-text emotional query to the most appropriate prophet story
	- Emotional intensity classification — predict EI level from situation description
	- Resolution type classification — predict RT from situation description

	---

	## Dataset Structure

	### Fields

	\| Field \| Type \| Description \|
	\|---\|---\|---\|
	\| `id` \| string \| Unique identifier e.g. `moses_003` \|
	\| `prophet_latin` \| string \| Prophet name in English e.g. `Moses` \|
	\| `prophet_arabic` \| string \| Prophet name in Arabic e.g. `موسى` \|
	\| `situation_en` \| string \| 3–5 sentence English description of the emotional situation \|
	\| `emotions` \| list[string] \| 2–5 primary emotion labels \|
	\| `EI` \| float \| Emotional Intensity 1.0–5.0 (half-point increments) \|
	\| `RT` \| int \| Resolution Type: 1=immediate, 2=gradual, 3=delayed \|
	\| `RT_label` \| string \| Human-readable RT label \|
	\| `child_paraphrase` \| string \| Child-friendly multilingual explanation and lesson \|
	\| `quran_ref` \| string \| Quranic verse or hadith reference \|
	\| `RL` \| int \| Recitation Level 1–3 \|
	\| `gender_of_protagonist` \| string \| `male` or `female` \|
	\| `annotator_agreed` \| bool \| Annotation confirmed by domain reviewer \|
	\| `version` \| string \| Corpus version \|

	### EI Distribution

	\| EI \| Label \| Count \|
	\|---\|---\|---\|
	\| 1 \| Mild everyday emotion \| 19 \|
	\| 2 \| Low-moderate distress \| 19 \|
	\| 3 \| Moderate distress \| 19 \|
	\| 4 \| High distress \| 24 \|
	\| 5 \| Severe crisis-level distress \| 19 \|

	### RT Distribution

	\| RT \| Label \| Count \|
	\|---\|---\|---\|
	\| 1 \| Immediate resolution \| 27 \|
	\| 2 \| Gradual resolution \| 29 \|
	\| 3 \| Delayed resolution (long patient endurance) \| 23 \|

	### Quranic Figures Covered

	Prophets: Adam, Idris, Noah, Hud, Salih, Abraham, Lot, Ishmael, Isaac, Jacob, Joseph, Job, Shuaib, Moses, Aaron, David, Solomon, Elijah, Elisha, Jonah, Zachariah, John, Jesus, Muhammad.

	Key figures (non-prophets): Mary, Hagar.

	---

	## Example Entry

	```json
	{
	"id": "joseph_003",
	"prophet_latin": "Joseph",
	"prophet_arabic": "يوسف",
	"situation_en": "Joseph was separated from his beloved father Jacob
	and thrown into a well by his own brothers out of jealousy.
	He was then sold into slavery in Egypt, far from everything
	he loved. Despite years of separation, Joseph never lost hope
	in Allah or stopped praying for his family.",
	"emotions": ["grief", "loneliness", "abandonment", "yearning", "patient_hope"],
	"EI": 3.0,
	"RT": 3,
	"RT_label": "delayed_resolution",
	"child_paraphrase": "Prophet Yusuf missed his father every single
	day for many years. He never stopped loving him even when they
	were far apart. Allah never forgot Yusuf, and one day reunited
	them in a way more beautiful than either could have imagined.",
	"quran_ref": "12:15-18",
	"RL": 2,
	"gender_of_protagonist": "male",
	"annotator_agreed": true,
	"version": "1.0"
	}
	```

	---

	## Usage

	```python
	from datasets import load_dataset

	ds = load_dataset("lamyaa/QESC")

	# All entries
	print(f"Total entries: {len(ds['train'])}")

	# Filter by EI level
	mild = [e for e in ds['train'] if e['EI'] <= 2.0]
	severe = [e for e in ds['train'] if e['EI'] == 5.0]

	# Filter by prophet
	joseph = [e for e in ds['train'] if e['prophet_latin'] == 'Joseph']

	# Filter by resolution type
	immediate = [e for e in ds['train'] if e['RT'] == 1]
	```

	### Use with CASS retrieval

	```python
	# See full code in: https://github.com/lsadouk/CASS

	# English query
	retrieve("I feel sad and nobody understands me")
	# → joseph_003 (EI=3, RT=3)

	# French query
	retrieve("Je me sens abandonné par mes amis")
	# → muhammad_002 (EI=3, RT=2)

	# Moroccan Darija query
	retrieve("ma kaynch had li yfahmni")
	# → shuaib_001 (EI=3, RT=3)
	```

	---

	## Safety Properties

	QESC is designed with child safety in mind. The EI annotation enables the CASS framework to prevent emotionally dangerous matches:

	- A child at EI=1 (mild embarrassment) will never receive an EI=5 story (extreme suffering)
	- A child at EI=5 (severe crisis) will never receive an EI=1 story (gentle everyday lesson)
	- Safety filtering is automatic — no user configuration required

	Across 5 diagnostic queries, CASS suppressed 27 of 28 emotionally dangerous entries with a mean rank drop of 37 positions and maximum of 62 positions.

	---

	## Annotation Guidelines

	EI scale:
	- EI=1: Mild everyday emotion — embarrassment, small disappointment, gentle worry
	- EI=2: Low-moderate distress — social exclusion, mild loneliness, minor conflict
	- EI=3: Moderate distress — betrayal, sustained loneliness, public humiliation
	- EI=4: High distress — acute fear, grief, unjust accusation, family conflict
	- EI=5: Severe crisis — extreme suffering, hopelessness, years of loss

	RT scale:
	- RT=1: Immediate resolution — comfort or answer arrives quickly (within the same scene)
	- RT=2: Gradual resolution — situation improves over weeks or months
	- RT=3: Delayed resolution — resolution requires long patient endurance (years)

	---

	## Citation

	```bibtex
	@article{sadouk2025cass,
	title = {CASS: A Context-Aware Semantic Similarity Framework
	for Safe Retrieval in Child-Facing Educational Applications},
	author = {Sadouk, Lamyaa AND Gadi, Taoufiq},
	journal = {Education and Information Technologies},
	year = {2025},
	publisher = {Springer}
	}
	```

	---

	## Dataset Card Authors

	Lamyaa Sadouk
	Ecole Marocaine des Sciences de l'Ingénieur, Casablanca, Morocco

	Taoufiq Gadi
	Laboratoire de Recherche en Mathématique, Informatique et Sciences de l'Ingénieur (MISI), Morocco

	## License

	[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — Attribution-NonCommercial 4.0 International

	You may use and share this dataset for research and educational purposes with attribution. Commercial use is not permitted without explicit permission from the author.