metadata

annotations_creators:
  - expert-generated
language_creators:
  - other
language:
  - pl
license:
  - 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)

Measurements: F1-score (seqeval)

Example:

Input: ['Zarejestruj', 'się', 'jako', 'bezrobotny', '.']

Input (translated by DeepL): Register as unemployed.

Output: ['impt', 'qub', 'conj', 'subst', 'interp']

Data splits

Subset	Cardinality (sentences)
train	78219
dev	0
test	7444

Class distribution

Class	train	dev	test
subst	0.27345	-	0.27656
interp	0.18101	-	0.17944
adj	0.10611	-	0.10919
prep	0.09567	-	0.09547
qub	0.05670	-	0.05491
fin	0.04939	-	0.04648
praet	0.04409	-	0.04348
conj	0.03711	-	0.03724
adv	0.03512	-	0.03333
inf	0.01591	-	0.01547
comp	0.01476	-	0.01439
num	0.01322	-	0.01436
ppron3	0.01111	-	0.01018
ppas	0.01086	-	0.01085
ger	0.00961	-	0.01050
brev	0.00856	-	0.01181
ppron12	0.00670	-	0.00665
aglt	0.00629	-	0.00602
pred	0.00539	-	0.00540
pact	0.00454	-	0.00452
bedzie	0.00229	-	0.00243
pcon	0.00218	-	0.00189
impt	0.00203	-	0.00226
siebie	0.00177	-	0.00158
imps	0.00174	-	0.00177
interj	0.00131	-	0.00102
xxx	0.00070	-	0.00048
adjp	0.00069	-	0.00065
winien	0.00068	-	0.00057
adja	0.00048	-	0.00058
pant	0.00012	-	0.00018
burk	0.00011	-	0.00006
numcol	0.00011	-	0.00013
depr	0.00010	-	0.00004
adjc	0.00007	-	0.00008

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': {...}}