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:

['Najwyraźniej', 'źle', 'ocenił', 'odległość', ',', 'bo', 'zderzył', 'się', 'z', 'jadącą', 'z', 'naprzeciwka', 'ciężarową', 'scanią', '.'] → ['qub', 'adv', 'praet', 'subst', 'interp', 'comp', 'praet', 'qub', 'prep', 'pact', 'prep', 'burk', 'adj', 'subst', 'interp']

Measurements:

Data splits

Subset	Cardinality (sentences)
train	78219
test	7444

Class distribution in train

Class	Fraction of tokens
subst	0.27345
interp	0.18101
adj	0.10611
prep	0.09567
qub	0.05670
fin	0.04939
praet	0.04409
conj	0.03711
adv	0.03512
inf	0.01591
comp	0.01476
num	0.01322
ppron3	0.01111
ppas	0.01086
ger	0.00961
brev	0.00856
ppron12	0.00670
aglt	0.00629
pred	0.00539
pact	0.00454
bedzie	0.00229
pcon	0.00218
impt	0.00203
siebie	0.00177
imps	0.00174
interj	0.00131
xxx	0.00070
adjp	0.00069
winien	0.00068
adja	0.00048
pant	0.00012
burk	0.00011
numcol	0.00011
depr	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])

# {'id': '130-2-900005_morph_49.49-s',
#  'pos_tags': [16, 4, 3, 30, 12, 18, 3, 16, 14, 6, 14, 26, 1, 30, 12],
#  'tokens': ['Najwyraźniej',
#             'źle',
#             'ocenił',
#             'odległość',
#             ',',
#             'bo',
#             'zderzył',
#             'się',
#             'z',
#             'jadącą',
#             'z',
#             'naprzeciwka',
#             'ciężarową',
#             'scanią',
#             '.']}

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': {...}}