File size: 12,697 Bytes
227c21b 1473009 15cc968 227c21b 15cc968 227c21b 1473009 227c21b 15cc968 bce87b6 227c21b bce87b6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 |
---
dataset_info:
- config_name: ee
features:
- name: id
dtype: string
- name: text
dtype: string
- name: entity_mentions
list:
- name: id
dtype: string
- name: text
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: char_start
dtype: int64
- name: char_end
dtype: int64
- name: type
dtype: string
- name: event_mentions
list:
- name: id
dtype: string
- name: trigger
struct:
- name: text
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: char_start
dtype: int64
- name: char_end
dtype: int64
- name: arguments
list:
- name: text
dtype: string
- name: start
dtype: int64
- name: end
dtype: int64
- name: char_start
dtype: int64
- name: char_end
dtype: int64
- name: role
dtype: string
- name: type
dtype: string
- name: event_type
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence: string
- name: lemma
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 6532239
num_examples: 1861
- name: validation
num_bytes: 792697
num_examples: 228
- name: test
num_bytes: 802322
num_examples: 230
download_size: 3171788
dataset_size: 8127258
- config_name: ner
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 2062754
num_examples: 1861
- name: validation
num_bytes: 250635
num_examples: 228
- name: test
num_bytes: 255164
num_examples: 230
download_size: 736425
dataset_size: 2568553
- config_name: re
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: entities
sequence:
sequence: int64
- name: entity_roles
sequence: string
- name: entity_types
sequence: string
- name: event_type
dtype: string
- name: entity_ids
sequence: string
splits:
- name: train
num_bytes: 2116771
num_examples: 1007
- name: validation
num_bytes: 265248
num_examples: 129
- name: test
num_bytes: 238094
num_examples: 128
download_size: 801404
dataset_size: 2620113
configs:
- config_name: ee
data_files:
- split: train
path: ee/train-*
- split: validation
path: ee/validation-*
- split: test
path: ee/test-*
- config_name: ner
data_files:
- split: train
path: ner/train-*
- split: validation
path: ner/validation-*
- split: test
path: ner/test-*
- config_name: re
data_files:
- split: train
path: re/train-*
- split: validation
path: re/validation-*
- split: test
path: re/test-*
license: cc-by-4.0
task_categories:
- text-classification
- token-classification
language:
- de
tags:
- finance
- relation-extraction
- event-extraction
- traffic
- industry
pretty_name: SmartData Corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for SmartData Corpus
## Dataset Description
- **Repository:** [https://github.com/dfki-nlp/smartdata-corpus](https://github.com/dfki-nlp/smartdata-corpus)
- **Paper:** [A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events](https://www.dfki.de/web/forschung/projekte-publikationen/publikation/9427/)
### Dataset Summary
SmartData Corpus is a German-language dataset which is human-annotated with entity types and a set of 15 traffic- and
industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes.
The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and
railway companies.
This version of the dataset loader provides configurations for:
- Named Entity Recognition (`ner`): NER tags use the `BIO` tagging scheme
- Relation Extraction (`re`): n-ary Relation Extraction
- Event Extraction (`ee`): formatted similar to https://github.com/nlpcl-lab/ace2005-preprocessing?tab=readme-ov-file#format
For more details see https://github.com/dfki-nlp/smartdata-corpus and https://www.dfki.de/web/forschung/projekte-publikationen/publikation/9427/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition, n-ary Relation Extraction, Event Extraction
- **Leaderboards:**
### Languages
German
## Dataset Structure
### Data Instances
#### ner
An example of 'train' looks as follows.
```json
{
"id": "671734738147758080",
"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
"ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
}
```
#### re
An example of 'train' looks as follows.
```json
{
"id": "671734738147758080_0",
"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
"entities": [[0, 1], [2, 4], [5, 7], [13, 14], [17, 18], [19, 20]],
"entity_roles": ["location", "start_loc", "end_loc", "trigger", "end_date", "no_arg"],
"entity_types": ["LOCATION_STREET", "LOCATION", "LOCATION", "TRIGGER", "DATE", "LOCATION_STREET"],
"event_type": "Obstruction",
"entity_ids": ["c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "c/ca3befa0-92da-4ff9-b34d-ec351854cdda"]
}
```
#### ee
An example of 'train' looks as follows.
```json
{
"id": "671734738147758080",
"text": "A1 Zwischen AS Munsbach und AS Flaxweiler Bauarbeiten, rechter Fahrstreifen gesperrt, Verkehrsbehinderung, Dauer: 02.12.2015... #ACL_A1\n",
"entity_mentions": [
{"id": "c/ac611f0a-d610-4ab2-9ddf-00132d9374b5", "text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "type": "LOCATION_STREET"},
{"id": "c/3e01d530-58c4-4f47-9ab3-082a58e8299b", "text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "type": "LOCATION"},
{"id": "c/cb6975e8-4409-4bdf-a491-de398b3c3263", "text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "type": "LOCATION"},
{"id": "c/684a0ccd-06ff-4a8f-a90f-bdef169077dc", "text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105, "type": "TRIGGER"},
{"id": "c/166acddb-0f4d-48eb-98f6-a8b490f2e578", "text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "type": "DATE"},
{"id": "c/ca3befa0-92da-4ff9-b34d-ec351854cdda", "text": "#ACL_A1", "start": 19, "end": 20, "char_start": 128, "char_end": 135, "type": "LOCATION_STREET"}
],
"event_mentions": [
{
"id": "r/802a82c2-c214-4429-b9f1-bf56e46674ee",
"trigger": {
"text": "Verkehrsbehinderung", "start": 13, "end": 14, "char_start": 86, "char_end": 105
},
"arguments": [
{"text": "02.12.2015", "start": 17, "end": 18, "char_start": 114, "char_end": 124, "role": "end_date", "type": "date"},
{"text": "AS Flaxweiler", "start": 5, "end": 7, "char_start": 28, "char_end": 41, "role": "end_loc", "type": "location"},
{"text": "AS Munsbach", "start": 2, "end": 4, "char_start": 12, "char_end": 23, "role": "start_loc", "type": "location"},
{"text": "A1", "start": 0, "end": 1, "char_start": 0, "char_end": 2, "role": "location", "type": "location-street"}
],
"event_type": "Obstruction"
}
],
"tokens": ["A1", "Zwischen", "AS", "Munsbach", "und", "AS", "Flaxweiler", "Bauarbeiten", ",", "rechter", "Fahrstreifen", "gesperrt", ",", "Verkehrsbehinderung", ",", "Dauer", ":", "02.12.2015", "...", "#ACL_A1"],
"pos_tags": ["CARD", "APPR", "NE", "NE", "KON", "NE", "NE", "NN", "$,", "ADJA", "NN", "VVPP", "$,", "NN", "$,", "NN", "$.", "CARD", "$[", "CARD"],
"lemma": ["a1", "zwischen", "as", "munsbach", "und", "as", "flaxweiler", "bauarbeiten", ",", "rechter", "fahrstreifen", "gesperrt", ",", "verkehrsbehinderung", ",", "dauer", ":", "02.12.2015", "...", "#acl_a1"],
"ner_tags": ["B-LOCATION_STREET", "O", "B-LOCATION", "I-LOCATION", "O", "B-LOCATION", "I-LOCATION", "O", "O", "O", "O", "O", "O", "B-TRIGGER", "O", "O", "O", "B-DATE", "O", "B-LOCATION_STREET"]
}
```
### Data Fields
#### ner
- `id`: example identifier, a `string` feature.
- `tokens`: list of tokens, a `list` of `string` features.
- `ner_tags`: list of NER tags, a `list` of `string` features.
#### re
- `id`: example identifier, a `string` feature.
- `text`: example text, a `string` feature.
- `tokens`: list of tokens, a `list` of `string` features.
- `entities`: a list of token spans, a `list` of `int64` features.
- `entity_roles`: a `list` of entity roles, a list of `string` features.
- `event_type`: the event type, a `string` feature.
- `entity_ids`: list of entity ids, a `list` of `string` features.
#### ee
- `id`: example identifier, a `string` feature.
- `text`: example text, a `string` feature.
- `entity_mentions`: a `list` of `struct` features.
- `text`: a `string` feature.
- `start`: token offset start, a `int64` feature.
- `end`: token offset end, a `int64` feature.
- `char_start`: character offset start, a `int64` feature.
- `char_end`: character offset end, a `int64` feature.
- `type`: entity type, a `string` feature.
- `id`: entity id, a `string` feature.
- `event_mentions`: a list of `struct` features.
- `id`: event identifier, a `string` feature.
- `trigger`: a `struct` feature.
- `text`: a `string` feature.
- `start`: token offset start, a `int64` feature.
- `end`: token offset end, a `int64` feature.
- `char_start`: character offset start, a `int64` feature.
- `char_end`: character offset end, a `int64` feature.
- `arguments`: a list of `struct` features.
- `text`: a `string` feature.
- `start`: token offset start, a `int64` feature.
- `end`: token offset end, a `int64` feature.
- `char_start`: character offset start, a `int64` feature.
- `char_end`: character offset end, a `int64` feature.
- `role`: role of the argument, a `string` feature.
- `type`: entity type of the argument, a `string` feature.
- `event_type`: a classification label, a `string` feature.
- `tokens`: list of tokens, a `list` of `string` features.
- `pos_tags`: list of part-of-speech tags, a `list` of `string` features.
- `lemma`: list of lemmatized tokens, a `list` of `string` features.
- `ner_tags`: a `list` of NER tags, a list of `string` features.
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
**BibTeX:**
```
@InProceedings{SCHIERSCH18.85,
author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {May 7-12, 2018},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english}
}
```
**APA:**
- Schiersch, M., Mironova, V., Schmitt, M., Thomas, P., Gabryszak, A., & Hennig, L. (2018). A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events. In N. Calzolari (Conference chair), K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. Unknown). Miyazaki, Japan: European Language Resources Association (ELRA). ISBN: 979-10-95546-00-9.
### Contributions |