---
license: cc-by-sa-4.0
language:
- sv
tags:
- readability
- text-complexity
- swedish
- lix
- linguistics
- nlp
task_categories:
- text-classification
- text-generation
pretty_name: Swedish Text Complexity
size_categories:
- 1K<n<10K
---

# Swedish Text Complexity Dataset

A corpus of Swedish texts annotated with readability and linguistic complexity metrics, created by the [Department of Linguistics and Philology at Uppsala University](https://www.uu.se/en/department/linguistics-and-philology).

## Dataset Description

This dataset contains Swedish text passages annotated with multiple complexity metrics, designed to support research in:

- **Controllable text generation** - Train LLMs to generate text at specific reading levels
- **Educational NLP** - Match texts to student reading proficiency
- **Text simplification** - Develop automatic simplification systems
- **Accessibility research** - Create tools for readers with different needs
- **Readability research** - Study linguistic factors affecting comprehension

## Metrics Included

| Metric | Description | Range |
|--------|-------------|-------|
| **LIX** | Läsbarhetsindex (Swedish readability index) | ~20-70+ |
| **OVIX** | Ordvariationsindex (lexical variation) | Higher = more varied vocabulary |
| **Nominal Ratio** | Noun/verb density ratio | Higher = more nominal style |
| **ASL** | Average Sentence Length | words per sentence |
| **AWL** | Average Word Length | characters per word |
| **LW%** | Long Word Percentage | % of words >6 characters |
| **TTR** | Type-Token Ratio | 0-1 (lexical diversity) |

### LIX Score Interpretation

| Score | Category | Typical Examples |
|-------|----------|------------------|
| < 25 | Very Easy | Children's books |
| 25-30 | Easy | Young adult fiction |
| 30-40 | Medium | Newspapers, popular fiction |
| 40-50 | Difficult | Official documents, non-fiction |
| 50-60 | Very Difficult | Academic texts |
| > 60 | Extremely Difficult | Legal, specialized academic |

## Dataset Structure

```python
{
    "id": "wikipedia_sv-a1b2c3d4",
    "text": "Stockholms tunnelbana öppnades 1950...",
    "source": "wikipedia_sv",
    "genre": "encyclopedia",
    "year": null,
    "author": "Wikipedia contributors",
    "license": "CC-BY-SA-4.0",
    "metrics_num_sentences": 5,
    "metrics_num_words": 87,
    "metrics_num_characters": 523,
    "metrics_num_long_words": 24,
    "metrics_num_unique_words": 71,
    "metrics_lix": 42.6,
    "metrics_lix_category": "difficult",
    "metrics_ovix": 78.3,
    "metrics_nominal_ratio": 1.45,
    "metrics_avg_sentence_length": 17.4,
    "metrics_avg_word_length": 6.01,
    "metrics_long_word_pct": 27.6,
    "metrics_type_token_ratio": 0.816
}
```

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("LingFilUU/swedish-text-complexity")
```

### Filtering by Complexity

```python
# Get only easy texts (LIX < 30)
easy_texts = dataset.filter(lambda x: x["metrics_lix"] < 30)

# Get difficult academic-style texts
difficult = dataset.filter(
    lambda x: x["metrics_lix"] > 50 and x["metrics_nominal_ratio"] > 1.5
)
```

### Training for Controllable Generation

```python
# Add complexity labels for conditional generation
def add_complexity_token(example):
    lix = example["metrics_lix"]
    if lix < 30:
        prefix = "<easy>"
    elif lix < 45:
        prefix = "<medium>"
    else:
        prefix = "<difficult>"
    example["text_with_prefix"] = f"{prefix} {example['text']}"
    return example

dataset = dataset.map(add_complexity_token)
```

## Data Sources

Texts in this dataset are sourced from openly-licensed Swedish corpora:

- **Swedish Wikipedia** (CC-BY-SA-4.0)
- **Språkbanken resources** (various open licenses)
- **Project Runeberg** (public domain)

## Methodology

### LIX Calculation

The LIX (Läsbarhetsindex) formula, developed by Carl-Hugo Björnsson (1968):

```
LIX = (words / sentences) + (long_words × 100 / words)
```

Where `long_words` = words with more than 6 characters.

### OVIX Calculation

The OVIX (Ordvariationsindex) formula:

```
OVIX = log(tokens) / log(2 - log(types) / log(tokens))
```

### Nominal Ratio

Calculated using spaCy's Swedish POS tagger:

```
NR = (nouns + prepositions + participles) / (verbs + adverbs + pronouns)
```

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{lingfiluu_swedish_text_complexity,
  author = {Department of Linguistics and Philology, Uppsala University},
  title = {Swedish Text Complexity Dataset},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/LingFilUU/swedish-text-complexity}
}
```

## References

- Björnsson, C.H. (1968). *Läsbarhet*. Stockholm: Liber.
- Hultman, T.G., & Westman, M. (1977). *Gymnasistsvenska*. Lund: Liber Läromedel.
- [Språkbanken Text](https://spraakbanken.gu.se/en)

## License

The dataset compilation is released under CC-BY-SA-4.0. Individual texts retain their original licenses as noted in the `license` field.

## Contact

Department of Linguistics and Philology
Uppsala University
https://www.uu.se/en/department/linguistics-and-philology