|
--- |
|
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) |
|
|
|
***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](https://huggingface.co/datasets/clarin-pl/nkjp-pos) |
|
|
|
[Source](http://clip.ipipan.waw.pl/NationalCorpusOfPolish) |
|
|
|
[Paper](http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf) |
|
|
|
## Examples |
|
|
|
### Loading |
|
|
|
```python |
|
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 |
|
|
|
```python |
|
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': {...}} |
|
``` |