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--- |
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license: cc-by-4.0 |
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language: |
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- sv |
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task_categories: |
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- text-generation |
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- token-classification |
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tags: |
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- swedish |
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- government-reports |
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- dependency-parsing |
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- universal-dependencies |
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- nlp |
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size_categories: |
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- 100K<n<1M |
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source_datasets: |
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- original |
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--- |
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# SOU Corpus - Swedish Government Official Reports |
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Cleaned and dependency-parsed Swedish Government Official Reports (Statens offentliga utredningar) from 1994-2020. |
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## Dataset Description |
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This dataset contains sentence-segmented and dependency-parsed text from Swedish Government Official Reports. The original documents were cleaned, processed, and annotated with Universal Dependencies-style parsing. |
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### Fields |
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- **document_id**: Original document identifier (can be linked to Riksdagen open data) |
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- **text_type**: Type of text section |
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- `full_text`: Main report body |
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- `summary_swedish`: Standard Swedish summary |
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- `summary_simple_swedish`: Simple Swedish (lättläst) summary |
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- `summary_english`: English summary |
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- **section**: Section header from the document |
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- **text**: Plain text sentence |
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- **parsed**: Dependency-parsed sentence (token//POS//deprel//head format) |
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### Parsed Format |
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Each token in the `parsed` field follows the format: |
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``` |
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word//POS_TAG//DEPENDENCY_RELATION//HEAD_INDEX |
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``` |
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Example: |
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``` |
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Sverige//PM|NOM//nsubj//3 är//VB|PRS|AKT//cop//3 ett//DT|NEU|SIN|IND//det//3 land//NN|NEU|SIN|IND|NOM//ROOT//3 |
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``` |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("UppsalaNLP/sou-corpus") |
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# Access train/test splits |
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train = dataset["train"] |
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test = dataset["test"] |
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# Example |
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print(train[0]["text"]) |
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print(train[0]["section"]) |
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print(train[0]["text_type"]) |
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``` |
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### Extract Tokens |
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```python |
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def parse_tokens(parsed_str): |
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tokens = [] |
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for t in parsed_str.split(' '): |
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parts = t.split('//') |
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if len(parts) >= 4: |
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tokens.append({ |
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'word': parts[0], |
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'pos': parts[1], |
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'deprel': parts[2], |
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'head': int(parts[3]) if parts[3].isdigit() else 0 |
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}) |
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return tokens |
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tokens = parse_tokens(train[0]["parsed"]) |
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``` |
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## Source |
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Documents obtained from [Riksdagens öppna data](http://data.riksdagen.se). Original document URLs follow the pattern: |
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`https://data.riksdagen.se/dokument/{document_id}.html` |
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## Citation |
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```bibtex |
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@inproceedings{durlich-etal-2022-cause, |
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title = "Cause and Effect in Governmental Reports: Two Data Sets for Causality Detection in Swedish", |
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author = "D{\"u}rlich, Luise and Reimann, Sebastian and Finnveden, Gustav and Nivre, Joakim and Stymne, Sara", |
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booktitle = "Proceedings of the First Workshop on Natural Language Processing for Political Sciences", |
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month = jun, |
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year = "2022", |
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address = "Marseilles, France" |
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} |
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``` |
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## License |
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This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
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## Links |
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- [Uppsala NLP](https://huggingface.co/UppsalaNLP) |
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- [GitHub Repository](https://github.com/UppsalaNLP/SOU-corpus) |
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- [Riksdagen Open Data](http://data.riksdagen.se) |
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