README.md

---
dataset_info:
  features:
  - name: id_doc
    dtype: string
  - name: words
    sequence:
      sequence:
        sequence: string
  - name: lemmas
    sequence:
      sequence:
        sequence: string
  - name: msds
    sequence:
      sequence:
        sequence: string
  - name: ne_tags
    sequence:
      sequence:
        sequence: string
  - name: mentions
    list:
    - name: id_mention
      dtype: string
    - name: mention_data
      struct:
      - name: idx_par
        dtype: uint32
      - name: idx_sent
        dtype: uint32
      - name: word_indices
        sequence: uint32
      - name: global_word_indices
        sequence: uint32
  - name: coref_clusters
    sequence:
      sequence: string
  splits:
  - name: train
    num_bytes: 21547216
    num_examples: 756
  download_size: 21892324
  dataset_size: 21547216
license: cc-by-sa-4.0
language:
- sl
pretty_name: SentiCoref
size_categories:
- n<1K
---

# Dataset card for SentiCoref

### Usage
```
import datasets
data = datasets.load_dataset("cjvt/senticoref", trust_remote_code=True)
```

### Dataset Summary
The dataset contains the SentiCoref corpus, annotated for coreference. It is part of the SUK training bundle of corpora.
For more details please check the paper or the [Clarin repository](http://hdl.handle.net/11356/1959) from which this dataset is being loaded.

## Dataset Structure

### Data Instances

```
{
	'id_doc': 'senticoref1', 
	'words': [
		[
			['Evropska', 'komisija', 'mora', 'narediti', 'analizo', 'vzrokov', 'rasti', 'cen', 'hrane', ',', 'menita', 'kmetijski', 'minister', 'Jarc', 'in', 'njegov', 'francoski', 'kolega', '.'], 
			['Bo', 'evropska', 'komisija', 'analizirala', 'vzroke', 'rasti', 'cen', 'hrane', '.'], 
			...
		],
		...
	], 
	'lemmas': [
		[
			['evropski', 'komisija', 'morati', 'narediti', 'analiza', 'vzrok', 'rast', 'cena', 'hrana', ',', 'meniti', 'kmetijski', 'minister', 'Jarc', 'in', 'njegov', 'francoski', 'kolega', '.'], 
			['biti', 'evropski', 'komisija', 'analizirati', 'vzrok', 'rast', 'cena', 'hrana', '.'],
			...
		]
	], 
	'msds': [
		[
			['mte:Ppnzei', 'mte:Sozei', 'mte:Ggnste', 'mte:Ggdn', 'mte:Sozet', 'mte:Sommr', 'mte:Sozer', 'mte:Sozmr', 'mte:Sozer', 'mte:U', 'mte:Ggnstd', 'mte:Ppnmeid', 'mte:Somei', 'mte:Slmei', 'mte:Vp', 'mte:Zstmeiem', 'mte:Ppnmeid', 'mte:Somei', 'mte:U'], 
			['mte:Gp-pte-n', 'mte:Ppnzei', 'mte:Sozei', 'mte:Ggvd-ez', 'mte:Sommt', 'mte:Sozer', 'mte:Sozmr', 'mte:Sozer', 'mte:U'], 
			...
		], 
		...
	],
	'ne_tags': [
		[
			['B-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-PER', 'O', 'O', 'O', 'O', 'O'], 
			['O', 'B-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'O'], 
			...
		], 
		...
	], 
	'mentions': [
		{'id_mention': 'senticoref1.1.1.ne1', 'mention_data': {'idx_par': 0, 'idx_sent': 0, 'word_indices': [0, 1], 'global_word_indices': [0, 1]}}, 
		...
	], 
	'coref_clusters': [
		['senticoref1.1.1.ne1', 'senticoref1.1.2.ne1', 'senticoref1.1.3.ne1'], 
		['senticoref1.1.1.phr52-1', 'senticoref1.1.3.phr52-2', 'senticoref1.1.11.phr52-3'], 
		['senticoref1.1.1.t5', 'senticoref1.1.3.t6', 'senticoref1.1.11.t11', 'senticoref1.1.11.t17'], 
		['senticoref1.1.1.phr13-1', 'senticoref1.1.2.phr13-2'], 
		...
	]
}
```

### Data Fields

- `id_doc`: a string ID of the document (corresponds to file name in this case);  
- `words`: a `List[List[List[String]]]` containing document words;
- `lemmas`: a `List[List[List[String]]]` containing document lemmas;  
- `msds`: a `List[List[List[String]]]` containing document morphosyntactic features, encoded using MULTEXT-East V6;  
- `ne_tags`: a `List[List[List[String]]]` containing document named entity tags, encoded using IOB2 scheme;  
- `mentions`: a list of dicts for each mention. Each mention contains an ID (`id_mention`) and 
positions of words inside mention (determined by `idx_sent`, `word_indices`; 
or equivalently `global_word_indices` if sentences are flattened into a single list)
- `coref_clusters`: a list of lists of strings containing mention IDs contained inside each coreference cluster. 

## Additional Information

### Dataset Curators

Špela Arhar Holdt; et al. (please see http://hdl.handle.net/11356/1959 for the full list of contributors)

### Licensing Information

CC BY-SA 4.0

### Citation Information

```
@article{senticoref-paper,
  title={Neural coreference resolution for Slovene language},
  author={Matej Klemen and Slavko Žitnik},
  journal={Computer Science and Information Systems},
  year={2022},
  volume={19},
  pages={495-521}
}
```

```
@misc{suk-clarin,
    title = {Training corpus {SUK} 1.1},
    author = {Arhar Holdt, {\v S}pela and Krek, Simon and Dobrovoljc, Kaja and Erjavec, Toma{\v z} and Gantar, Polona and {\v C}ibej, Jaka and Pori, Eva and Ter{\v c}on, Luka and Munda, Tina and {\v Z}itnik, Slavko and Robida, Nejc and Blagus, Neli and Mo{\v z}e, Sara and Ledinek, Nina and Holz, Nanika and Zupan, Katja and Kuzman, Taja and Kav{\v c}i{\v c}, Teja and {\v S}krjanec, Iza and Marko, Dafne and Jezer{\v s}ek, Lucija and Zajc, Anja},
    url = {http://hdl.handle.net/11356/1959},
    note = {Slovenian language resource repository {CLARIN}.{SI}},
    copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
    issn = {2820-4042},
    year = {2024}
}
```

### Contributions

Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.