Datasets:
Tasks:
Other
Sub-tasks:
named-entity-recognition
Languages:
Polish
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pl
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 18K
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
pretty_name: KPWr-NER
tags:
- structure-prediction
KPWR-NER
Description
KPWR-NER is a part the Polish Corpus of Wrocław University of Technology (Korpus Języka Polskiego Politechniki Wrocławskiej). Its objective is named entity recognition for fine-grained categories of entities. It is the ‘n82’ version of the KPWr, which means that number of classes is restricted to 82 (originally 120). During corpus creation, texts were annotated by humans from various sources, covering many domains and genres.
Tasks (input, output and metrics)
Named entity recognition (NER) - tagging entities in text with their corresponding type.
Input ('tokens' column): sequence of tokens
Output ('ner' column): sequence of predicted tokens’ classes in BIO notation (82 possible classes, described in detail in the annotation guidelines)
Measurements: F1-score (seqeval)
Example:
Input: [‘Roboty’, ‘mają’, ‘kilkanaście’, ‘lat’, ‘i’, ‘pochodzą’, ‘z’, ‘USA’, ‘,’, ‘Wysokie’, ‘napięcie’, ‘jest’, ‘dużo’, ‘młodsze’, ‘,’, ‘powstało’, ‘w’, ‘Niemczech’, ‘.’]
Input (translated by DeepL): Robots are more than a dozen years old and come from the US, High Voltage is much younger, having been developed in Germany.
Output: [‘B-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘B-nam_loc_gpe_country’, ‘O’, ‘B-nam_pro_title’, ‘I-nam_pro_title’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘O’, ‘B-nam_loc_gpe_country’, ‘O’]
Data splits
| Subset | Cardinality (sentences) |
|---|---|
| train | 13959 |
| dev | 0 |
| test | 4323 |
Class distribution (without "O" and "I-*")
| Class | train | validation | test |
|---|---|---|---|
| B-nam_liv_person | 0.21910 | - | 0.21422 |
| B-nam_loc_gpe_city | 0.10101 | - | 0.09865 |
| B-nam_loc_gpe_country | 0.07467 | - | 0.08059 |
| B-nam_org_institution | 0.05893 | - | 0.06005 |
| B-nam_org_organization | 0.04448 | - | 0.05553 |
| B-nam_org_group_team | 0.03492 | - | 0.03363 |
| B-nam_adj_country | 0.03410 | - | 0.03747 |
| B-nam_org_company | 0.02439 | - | 0.01716 |
| B-nam_pro_media_periodic | 0.02250 | - | 0.01896 |
| B-nam_fac_road | 0.01995 | - | 0.02144 |
| B-nam_liv_god | 0.01934 | - | 0.00790 |
| B-nam_org_nation | 0.01739 | - | 0.01828 |
| B-nam_oth_tech | 0.01724 | - | 0.01377 |
| B-nam_pro_media_web | 0.01709 | - | 0.00903 |
| B-nam_fac_goe | 0.01596 | - | 0.01445 |
| B-nam_eve_human | 0.01573 | - | 0.01761 |
| B-nam_pro_title | 0.01558 | - | 0.00790 |
| B-nam_pro_brand | 0.01543 | - | 0.01038 |
| B-nam_org_political_party | 0.01264 | - | 0.01309 |
| B-nam_loc_gpe_admin1 | 0.01219 | - | 0.01445 |
| B-nam_eve_human_sport | 0.01174 | - | 0.01242 |
| B-nam_pro_software | 0.01091 | - | 0.02190 |
| B-nam_adj | 0.00963 | - | 0.01174 |
| B-nam_loc_gpe_admin3 | 0.00888 | - | 0.01061 |
| B-nam_pro_model_car | 0.00873 | - | 0.00587 |
| B-nam_loc_hydronym_river | 0.00843 | - | 0.01151 |
| B-nam_oth | 0.00775 | - | 0.00497 |
| B-nam_pro_title_document | 0.00738 | - | 0.01986 |
| B-nam_loc_astronomical | 0.00730 | - | - |
| B-nam_oth_currency | 0.00723 | - | 0.01151 |
| B-nam_adj_city | 0.00670 | - | 0.00948 |
| B-nam_org_group_band | 0.00587 | - | 0.00429 |
| B-nam_loc_gpe_admin2 | 0.00565 | - | 0.00813 |
| B-nam_loc_gpe_district | 0.00504 | - | 0.00406 |
| B-nam_loc_land_continent | 0.00459 | - | 0.00722 |
| B-nam_loc_country_region | 0.00459 | - | 0.00090 |
| B-nam_loc_land_mountain | 0.00414 | - | 0.00203 |
| B-nam_pro_title_book | 0.00384 | - | 0.00248 |
| B-nam_loc_historical_region | 0.00376 | - | 0.00497 |
| B-nam_loc | 0.00361 | - | 0.00090 |
| B-nam_eve | 0.00361 | - | 0.00181 |
| B-nam_org_group | 0.00331 | - | 0.00406 |
| B-nam_loc_land_island | 0.00331 | - | 0.00248 |
| B-nam_pro_media_tv | 0.00316 | - | 0.00158 |
| B-nam_liv_habitant | 0.00316 | - | 0.00158 |
| B-nam_eve_human_cultural | 0.00316 | - | 0.00497 |
| B-nam_pro_title_tv | 0.00309 | - | 0.00542 |
| B-nam_oth_license | 0.00286 | - | 0.00248 |
| B-nam_num_house | 0.00256 | - | 0.00248 |
| B-nam_pro_title_treaty | 0.00248 | - | 0.00045 |
| B-nam_fac_system | 0.00248 | - | 0.00587 |
| B-nam_loc_gpe_subdivision | 0.00241 | - | 0.00587 |
| B-nam_loc_land_region | 0.00226 | - | 0.00248 |
| B-nam_pro_title_album | 0.00218 | - | 0.00158 |
| B-nam_adj_person | 0.00203 | - | 0.00406 |
| B-nam_fac_square | 0.00196 | - | 0.00135 |
| B-nam_pro_award | 0.00188 | - | 0.00519 |
| B-nam_eve_human_holiday | 0.00188 | - | 0.00203 |
| B-nam_pro_title_song | 0.00166 | - | 0.00158 |
| B-nam_pro_media_radio | 0.00151 | - | 0.00068 |
| B-nam_pro_vehicle | 0.00151 | - | 0.00090 |
| B-nam_oth_position | 0.00143 | - | 0.00226 |
| B-nam_liv_animal | 0.00143 | - | 0.00248 |
| B-nam_pro | 0.00135 | - | 0.00045 |
| B-nam_oth_www | 0.00120 | - | 0.00451 |
| B-nam_num_phone | 0.00120 | - | 0.00045 |
| B-nam_pro_title_article | 0.00113 | - | - |
| B-nam_oth_data_format | 0.00113 | - | 0.00226 |
| B-nam_fac_bridge | 0.00105 | - | 0.00090 |
| B-nam_liv_character | 0.00098 | - | - |
| B-nam_pro_software_game | 0.00090 | - | 0.00068 |
| B-nam_loc_hydronym_lake | 0.00090 | - | 0.00045 |
| B-nam_loc_gpe_conurbation | 0.00090 | - | - |
| B-nam_pro_media | 0.00083 | - | 0.00181 |
| B-nam_loc_land | 0.00075 | - | 0.00045 |
| B-nam_loc_land_peak | 0.00075 | - | - |
| B-nam_fac_park | 0.00068 | - | 0.00226 |
| B-nam_org_organization_sub | 0.00060 | - | 0.00068 |
| B-nam_loc_hydronym | 0.00060 | - | 0.00023 |
| B-nam_loc_hydronym_sea | 0.00045 | - | 0.00068 |
| B-nam_loc_hydronym_ocean | 0.00045 | - | 0.00023 |
| B-nam_fac_goe_stop | 0.00038 | - | 0.00090 |
Citation
@inproceedings{broda-etal-2012-kpwr,
title = "{KPW}r: Towards a Free Corpus of {P}olish",
author = "Broda, Bartosz and
Marci{\'n}czuk, Micha{\l} and
Maziarz, Marek and
Radziszewski, Adam and
Wardy{\'n}ski, Adam",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/965_Paper.pdf",
pages = "3218--3222",
abstract = "This paper presents our efforts aimed at collecting and annotating a free Polish corpus. The corpus will serve for us as training and testing material for experiments with Machine Learning algorithms. As others may also benefit from the resource, we are going to release it under a Creative Commons licence, which is hoped to remove unnecessary usage restrictions, but also to facilitate reproduction of our experimental results. The corpus is being annotated with various types of linguistic entities: chunks and named entities, selected syntactic and semantic relations, word senses and anaphora. We report on the current state of the project as well as our ultimate goals.",
}
License
Creative Commons Attribution 3.0 Unported Licence
Links
KPWr annotation guidelines - named entities
Examples
Loading
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("clarin-pl/kpwr-ner")
pprint(dataset['train'][0])
# {'lemmas': ['roborally', 'czy', 'wysoki', 'napięcie', '?'],
# 'ner': [73, 160, 73, 151, 160],
# 'orth': ['subst:sg:nom:n',
# 'qub',
# 'adj:sg:nom:n:pos',
# 'subst:sg:nom:n',
# 'interp'],
# 'tokens': ['RoboRally', 'czy', 'Wysokie', 'napięcie', '?']}
Evaluation
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("clarin-pl/kpwr-ner")
references = dataset["test"]["ner"]
# generate random predictions
predictions = [
[
random.randrange(dataset["train"].features["ner"].feature.num_classes)
for _ in range(len(labels))
]
for labels in references
]
# transform to original names of labels
references_named = [
[dataset["train"].features["ner"].feature.names[label] for label in labels]
for labels in references
]
predictions_named = [
[dataset["train"].features["ner"].feature.names[label] for label in labels]
for labels in predictions
]
# utilise seqeval to evaluate
seqeval = load_metric("seqeval")
seqeval_score = seqeval.compute(
predictions=predictions_named, references=references_named, scheme="IOB2"
)
pprint(seqeval_score, depth=1)
# {'nam_adj': {...},
# 'nam_adj_city': {...},
# 'nam_adj_country': {...},
# 'nam_adj_person': {...},
# 'nam_eve': {...},
# 'nam_eve_human': {...},
# 'nam_eve_human_cultural': {...},
# 'nam_eve_human_holiday': {...},
# 'nam_eve_human_sport': {...},
# 'nam_fac_bridge': {...},
# 'nam_fac_goe': {...},
# 'nam_fac_goe_stop': {...},
# 'nam_fac_park': {...},
# 'nam_fac_road': {...},
# 'nam_fac_square': {...},
# 'nam_fac_system': {...},
# 'nam_liv_animal': {...},
# 'nam_liv_character': {...},
# 'nam_liv_god': {...},
# 'nam_liv_habitant': {...},
# 'nam_liv_person': {...},
# 'nam_loc': {...},
# 'nam_loc_astronomical': {...},
# 'nam_loc_country_region': {...},
# 'nam_loc_gpe_admin1': {...},
# 'nam_loc_gpe_admin2': {...},
# 'nam_loc_gpe_admin3': {...},
# 'nam_loc_gpe_city': {...},
# 'nam_loc_gpe_conurbation': {...},
# 'nam_loc_gpe_country': {...},
# 'nam_loc_gpe_district': {...},
# 'nam_loc_gpe_subdivision': {...},
# 'nam_loc_historical_region': {...},
# 'nam_loc_hydronym': {...},
# 'nam_loc_hydronym_lake': {...},
# 'nam_loc_hydronym_ocean': {...},
# 'nam_loc_hydronym_river': {...},
# 'nam_loc_hydronym_sea': {...},
# 'nam_loc_land': {...},
# 'nam_loc_land_continent': {...},
# 'nam_loc_land_island': {...},
# 'nam_loc_land_mountain': {...},
# 'nam_loc_land_peak': {...},
# 'nam_loc_land_region': {...},
# 'nam_num_house': {...},
# 'nam_num_phone': {...},
# 'nam_org_company': {...},
# 'nam_org_group': {...},
# 'nam_org_group_band': {...},
# 'nam_org_group_team': {...},
# 'nam_org_institution': {...},
# 'nam_org_nation': {...},
# 'nam_org_organization': {...},
# 'nam_org_organization_sub': {...},
# 'nam_org_political_party': {...},
# 'nam_oth': {...},
# 'nam_oth_currency': {...},
# 'nam_oth_data_format': {...},
# 'nam_oth_license': {...},
# 'nam_oth_position': {...},
# 'nam_oth_tech': {...},
# 'nam_oth_www': {...},
# 'nam_pro': {...},
# 'nam_pro_award': {...},
# 'nam_pro_brand': {...},
# 'nam_pro_media': {...},
# 'nam_pro_media_periodic': {...},
# 'nam_pro_media_radio': {...},
# 'nam_pro_media_tv': {...},
# 'nam_pro_media_web': {...},
# 'nam_pro_model_car': {...},
# 'nam_pro_software': {...},
# 'nam_pro_software_game': {...},
# 'nam_pro_title': {...},
# 'nam_pro_title_album': {...},
# 'nam_pro_title_article': {...},
# 'nam_pro_title_book': {...},
# 'nam_pro_title_document': {...},
# 'nam_pro_title_song': {...},
# 'nam_pro_title_treaty': {...},
# 'nam_pro_title_tv': {...},
# 'nam_pro_vehicle': {...},
# 'overall_accuracy': 0.006156203762418094,
# 'overall_f1': 0.0009844258777797407,
# 'overall_precision': 0.0005213624939842789,
# 'overall_recall': 0.008803611738148984}