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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- expert-generated |
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language: |
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- pl |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: NLPre-PL_dataset |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- original |
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tags: |
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- National Corpus of Polish |
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- Narodowy Korpus Języka Polskiego |
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task_categories: |
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- token-classification |
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task_ids: |
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- part-of-speech |
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- lemmatization |
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- parsing |
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dataset_info: |
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- config_name: nlprepl_by_name |
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features: |
|
- name: idx |
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dtype: string |
|
- name: text |
|
dtype: string |
|
- name: tokens |
|
sequence: string |
|
- name: lemmas |
|
sequence: string |
|
- name: upos |
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sequence: |
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class_label: |
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names: |
|
'0': NOUN |
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'1': PUNCT |
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'2': ADP |
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'3': NUM |
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'4': SYM |
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'5': SCONJ |
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'6': ADJ |
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'7': PART |
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'8': DET |
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'9': CCONJ |
|
'10': PROPN |
|
'11': PRON |
|
'12': X |
|
'13': _ |
|
'14': ADV |
|
'15': INTJ |
|
'16': VERB |
|
'17': AUX |
|
- name: xpos |
|
sequence: string |
|
- name: feats |
|
sequence: string |
|
- name: head |
|
sequence: string |
|
- name: deprel |
|
sequence: string |
|
- name: deps |
|
sequence: string |
|
- name: misc |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 0 |
|
num_examples: 69360 |
|
- name: dev |
|
num_bytes: 0 |
|
num_examples: 7669 |
|
- name: test |
|
num_bytes: 0 |
|
num_examples: 8633 |
|
download_size: 3088237 |
|
dataset_size: 5120697 |
|
- config_name: nlprepl_by_type |
|
features: |
|
- name: idx |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
- name: tokens |
|
sequence: string |
|
- name: lemmas |
|
sequence: string |
|
- name: upos |
|
sequence: |
|
class_label: |
|
names: |
|
'0': NOUN |
|
'1': PUNCT |
|
'2': ADP |
|
'3': NUM |
|
'4': SYM |
|
'5': SCONJ |
|
'6': ADJ |
|
'7': PART |
|
'8': DET |
|
'9': CCONJ |
|
'10': PROPN |
|
'11': PRON |
|
'12': X |
|
'13': _ |
|
'14': ADV |
|
'15': INTJ |
|
'16': VERB |
|
'17': AUX |
|
- name: xpos |
|
sequence: string |
|
- name: feats |
|
sequence: string |
|
- name: head |
|
sequence: string |
|
- name: deprel |
|
sequence: string |
|
- name: deps |
|
sequence: string |
|
- name: misc |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 0 |
|
num_examples: 68943 |
|
- name: dev |
|
num_bytes: 0 |
|
num_examples: 7755 |
|
- name: test |
|
num_bytes: 0 |
|
num_examples: 8964 |
|
download_size: 3088237 |
|
dataset_size: 5120697 |
|
--- |
|
# Dataset Card for NLPre-PL – fairly divided version of NKJP1M |
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|
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### Dataset Summary |
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|
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This is the official NLPre-PL dataset - a uniformly paragraph-level divided version of NKJP1M corpus – the 1-million token balanced subcorpus of the National Corpus of Polish (Narodowy Korpus Języka Polskiego) |
|
|
|
The NLPre dataset aims at fairly dividing the paragraphs length-wise and topic-wise into train, development, and test sets. Thus, we ensure a similar number of segments |
|
distribution per paragraph and avoid the situation when paragraphs with a small (or large) number of segments are available only e.g. during test time. |
|
|
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We treat paragraphs as indivisible units (to ensure there is no data leakage between different dataset types). The paragraphs inherit the corresponding document's ID and type (a book, an article, etc.). |
|
|
|
We provide two variations of the dataset, based on the fair division of paragraphs: |
|
- fair by document's ID |
|
- fair by document's type |
|
|
|
### Creation of the dataset |
|
|
|
We investigate the distribution over the number of segments in each paragraph. Being Gaussian-like, we divide the paragraphs into 10 buckets of roughly similar size and then sample from them with respective ratios of 0.8 : 0.1 : 0.1 |
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(corresponding to training, development, and testing subsets). |
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This data selection technique assures a similar distribution of segment numbers per paragraph in our three subsets. We call it **fair_by_name** (shortly: **by_name**) |
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since it is divided equitably regarding the unique IDs of the documents. |
|
|
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For creating our second split, we also consider the type of document a paragraph belongs to. We first group paragraphs into categories equal to the document types, |
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and then we repeat the above-mentioned procedure per category. This provides us with a second split: **fair_by_type** (shortly: **by_type**). |
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|
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### Supported Tasks and Leaderboards |
|
|
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This resource can be mainly used for training the morphosyntactic analyzer models for Polish. It support such tasks as: lemmatization, part-of-speech recognition, dependency parsing. |
|
|
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### Languages |
|
|
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Polish (monolingual) |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
``` |
|
{'nkjp_text': 'NKJP_1M_1102000002', |
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'nkjp_par': 'morph_1-p', |
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'nkjp_sent': 'morph_1.18-s', |
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'tokens': ['-', 'Nie', 'mam', 'pieniędzy', ',', 'da', 'mi', 'pani', 'wywiad', '?'], |
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'lemmas': ['-', 'nie', 'mieć', 'pieniądz', ',', 'dać', 'ja', 'pani', 'wywiad', '?'], |
|
'cposes': [8, 11, 10, 9, 8, 10, 9, 9, 9, 8], |
|
'poses': [19, 25, 12, 35, 19, 12, 28, 35, 35, 19], |
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'tags': [266, 464, 213, 923, 266, 218, 692, 988, 961, 266], |
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'nps': [False, False, False, False, True, False, False, False, False, True], |
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'nkjp_ids': ['morph_1.9-seg', 'morph_1.10-seg', 'morph_1.11-seg', 'morph_1.12-seg', 'morph_1.13-seg', 'morph_1.14-seg', 'morph_1.15-seg', 'morph_1.16-seg', 'morph_1.17-seg', 'morph_1.18-seg']} |
|
``` |
|
|
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### Data Fields |
|
|
|
- `nkjp_text`, `nkjp_par`, `nkjp_sent` (strings): XML identifiers of the present text (document), paragraph and sentence in NKJP. (These allow to map the data point back to the source corpus and to identify paragraphs/samples.) |
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- `tokens` (sequence of strings): tokens of the text defined as in NKJP. |
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- `lemmas` (sequence of strings): lemmas corresponding to the tokens. |
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- `tags` (sequence of labels): morpho-syntactic tags according to Morfeusz2 tagset (1019 distinct tags). |
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- `poses` (sequence of labels): flexemic class (detailed part of speech, 40 classes) – the first element of the corresponding tag. |
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- `cposes` (sequence of labels): coarse part of speech (13 classes): all verbal and deverbal flexemic classes get mapped to a `V`, nominal – `N`, adjectival – `A`, “strange” (abbreviations, alien elements, symbols, emojis…) – `X`, rest as in `poses`. |
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- `nps` (sequence of booleans): `True` means that the corresponding token is not preceded by a space in the source text. |
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- `nkjp_ids` (sequence of strings): XML identifiers of particular tokens in NKJP (probably an overkill). |
|
|
|
### Data Splits |
|
|
|
#### Fair_by_name |
|
|
|
| | Train | Validation | Test | |
|
| ----- | ------ | ----- | ---- | |
|
| sentences | 69360 | 7669 | 8633 | |
|
| tokens | 984077 | 109900 | 121907 | |
|
|
|
#### Fair_by_type |
|
|
|
| | Train | Validation | Test | |
|
| ----- | ------ | ----- | ---- | |
|
| sentences | 68943 | 7755 | 8964 | |
|
| tokens | 978371 | 112454 | 125059 | |
|
|
|
|
|
## Licensing Information |
|
|
|
 This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/). |
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|
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<!-- |
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### Contributions |
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|
|
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
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--> |
