Datasets:
metadata
annotations_creators:
- expert-generated
language_creators:
- other
languages:
- pl
licenses:
- gpl-3.0
multilinguality:
- monolingual
pretty_name: nkjp-pos
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- part-of-speech-tagging
nkjp-pos
Description
NKJP-POS is a part the National Corpus of Polish (Narodowy Korpus Języka Polskiego). Its objective is part-of-speech tagging, e.g. nouns, verbs, adjectives, adverbs, etc. During the creation of corpus, texts of were annotated by humans from various sources, covering many domains and genres.
Tasks (input, output and metrics)
Part-of-speech tagging (POS tagging) - tagging words in text with their corresponding part of speech.
Input ('tokens' column): sequence of tokens
Output ('pos_tags' column): sequence of predicted tokens’ classes (35 possible classes, described in detail in the annotation guidelines)
example:
['Zarejestruj', 'się', 'jako', 'bezrobotny', '.'] → ['impt', 'qub', 'conj', 'subst', 'interp']
Measurements:
Data splits
| Subset | Cardinality (sentences) |
|---|---|
| train | 68528 |
| val | 8566 |
| test | 8566 |
Class distribution in train
| Class | Fraction of tokens |
|---|---|
| subst | 0.27295 |
| interp | 0.18381 |
| adj | 0.10607 |
| prep | 0.09533 |
| qub | 0.05633 |
| fin | 0.04895 |
| praet | 0.04385 |
| conj | 0.03685 |
| adv | 0.03498 |
| inf | 0.01586 |
| comp | 0.01465 |
| num | 0.01319 |
| ppron3 | 0.01090 |
| ppas | 0.01080 |
| ger | 0.00967 |
| brev | 0.00880 |
| ppron12 | 0.00668 |
| aglt | 0.00620 |
| pred | 0.00536 |
| pact | 0.00452 |
| bedzie | 0.00232 |
| pcon | 0.00216 |
| impt | 0.00201 |
| siebie | 0.00175 |
| imps | 0.00172 |
| interj | 0.00128 |
| xxx | 0.00067 |
| winien | 0.00066 |
| adjp | 0.00066 |
| adja | 0.00048 |
| pant | 0.00013 |
| depr | 0.00010 |
| burk | 0.00010 |
| numcol | 0.00010 |
| adjc | 0.00007 |
Citation
@book{przepiorkowski_narodowy_2012,
title = {Narodowy korpus języka polskiego},
isbn = {978-83-01-16700-4},
language = {pl},
publisher = {Wydawnictwo Naukowe PWN},
editor = {Przepiórkowski, Adam and Bańko, Mirosław and Górski, Rafał L. and Lewandowska-Tomaszczyk, Barbara},
year = {2012}
}
License
GNU GPL v.3
Links
Examples
Loading
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("clarin-pl/nkjp-pos")
pprint(dataset['train'][5000])
# {'full_pos_tags': ['fin:sg:ter:imperf',
# 'subst:sg:nom:f',
# 'adj:sg:nom:f:pos',
# 'interp'],
# 'lemmas': ['trwać', 'akcja', 'poszukiwawczy', '.'],
# 'morph': ['trwać|fin:sg:ter:imperf',
# 'akcja|subst:sg:nom:f',
# 'poszukiwawczy|adj:sg:nom:f:pos poszukiwawczy|adj:sg:voc:f:pos',
# '.|interp'],
# 'nps': ['', '', 'nps', ''],
# 'pos_tags': [12, 32, 0, 18],
# 'tokens': ['Trwa', 'akcja', 'poszukiwawcza', '.']}
Evaluation
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("clarin-pl/nkjp-pos")
references = dataset["test"]["pos_tags"]
# generate random predictions
predictions = [
[
random.randrange(dataset["train"].features["pos_tags"].feature.num_classes)
for _ in range(len(labels))
]
for labels in references
]
# transform to original names of labels
references_named = [
[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
for labels in references
]
predictions_named = [
[dataset["train"].features["pos_tags"].feature.names[label] for label in labels]
for labels in predictions
]
# transform to BILOU scheme
references_named = [
[f"U-{label}" if label != "O" else label for label in labels]
for labels in references_named
]
predictions_named = [
[f"U-{label}" if label != "O" else label for label in labels]
for labels in predictions_named
]
# utilise seqeval to evaluate
seqeval = load_metric("seqeval")
seqeval_score = seqeval.compute(
predictions=predictions_named,
references=references_named,
scheme="BILOU",
mode="strict",
)
pprint(seqeval_score, depth=1)
# {'adj': {...},
# 'adja': {...},
# 'adjc': {...},
# 'adjp': {...},
# 'adv': {...},
# 'aglt': {...},
# 'bedzie': {...},
# 'brev': {...},
# 'burk': {...},
# 'comp': {...},
# 'conj': {...},
# 'depr': {...},
# 'fin': {...},
# 'ger': {...},
# 'imps': {...},
# 'impt': {...},
# 'inf': {...},
# 'interj': {...},
# 'interp': {...},
# 'num': {...},
# 'numcol': {...},
# 'overall_accuracy': 0.027855061488566583,
# 'overall_f1': 0.027855061488566583,
# 'overall_precision': 0.027855061488566583,
# 'overall_recall': 0.027855061488566583,
# 'pact': {...},
# 'pant': {...},
# 'pcon': {...},
# 'ppas': {...},
# 'ppron12': {...},
# 'ppron3': {...},
# 'praet': {...},
# 'pred': {...},
# 'prep': {...},
# 'qub': {...},
# 'siebie': {...},
# 'subst': {...},
# 'winien': {...},
# 'xxx': {...}}