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

HuggingFace

Source

Paper

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': {...}}