File size: 5,403 Bytes
fb63763 7009073 fb63763 7009073 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
---
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. |