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Datasets:
clarin-knext
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wsd_polish_datasets

Tasks:
Token Classification
Sub-tasks:
word-sense-disambiguation
Languages:
Polish
Size:
1M<n<10M
License:
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metadata
annotations_creators:
  - expert-generated
language:
  - pl
language_creators:
  - expert-generated
  - found
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: wsd-polish-datasets
size_categories:
  - 1M<n<10M
source_datasets:
  - original
tags: []
task_categories:
  - token-classification
task_ids:
  - word-sense-disambiguation

Word Sense Disambiguation Corpora for Polish

Table of Contents

Dataset Description

Dataset Summary

WSD Polish Datasets is a comprehensive benchmark for word sense disambiguation (WSD) classification task written in low-resource language Polish. It consists of 6 distinct datasets, manually annotated based on plWordNet-4.5:

  • KPWr
  • KPWr-100
  • Sherlock (SPEC)
  • Skladnica
  • WikiGlex
  • EmoGlex
  • Walenty

Supported Tasks and Leaderboards

Word sense disambiguation task. We do not provide a leaderboard. However, we provide an example evaluation script for evaluating WSD models.

Languages

Polish language, PL

Dataset Structure

Data Instances

Data are structured in JSONL format, each single text sample is divided by sentence.

{
  "text": "Wpierw pani Hudson została zerwana z łóżka, po czym odegrała się na mnie, a ja - na tobie.",
  "tokens": [
    {
      "index": 0,
      "position": [ 0, 6 ],
      "orth": "Wpierw",
      "lemma": "wpierw",
      "pos": "adv",
      "ctag": "adv"
    },
    {
      "index": 1,
      "position": [ 7, 11 ],
      "orth": "pani",
      "lemma": "pani",
      "pos": "noun",
      "ctag": "subst:nom:f:sg"
    },
    {
      "index": 2,
      "position": [ 12, 18 ],
      "orth": "Hudson",
      "lemma": "Hudson",
      "pos": "noun",
      "ctag": "subst:nom:f:sg"
    },
    {
      "index": 3,
      "position": [ 19, 26 ],
      "orth": "została",
      "lemma": "zostać",
      "pos": "verb",
      "ctag": "praet:perf:f:sg"
    },
    {
      "index": 4,
      "position": [ 27, 34 ],
      "orth": "zerwana",
      "lemma": "zerwać",
      "pos": "verb",
      "ctag": "ppas:perf:nom:f:aff:sg"
    },
   <...>
  ],
  "phrases": [
    {
      "indices": [ 10, 11 ],
      "head": 10,
      "lemma": "odegrać się"
    }
  ],
  "wsd": [
      {
        "index": 0,
        "pl_sense": "wpierw.1.r",
        "plWN_syn_id": "01a4a067-aac5-11ed-aae5-0242ac130002",
        "plWN_lex_id": "f2757c30-aac4-11ed-aae5-0242ac130002",
        "plWN_syn_legacy_id": "477654",
        "plWN_lex_legacy_id": "718454",
        "PWN_syn_id": "00102736-r",
        "bn_syn_id": "bn:00115376r",
        "mapping_relation": "synonymy"
      },
      {
        "index": 1,
        "pl_sense": "pani.2.n",
        "plWN_syn_id": "f35fb1ed-aac4-11ed-aae5-0242ac130002",
        "plWN_lex_id": "d5145565-aac4-11ed-aae5-0242ac130002",
        "plWN_syn_legacy_id": "129",
        "plWN_lex_legacy_id": "20695",
        "PWN_syn_id": "10787470-n",
        "bn_syn_id": "bn:00001530n",
        "mapping_relation": "synonymy"
      },
   <...>
  ]
}

Data Fields

Description of json keys:

  • text: text of the sentence
  • tokens: list of tokens made by tokenization process
    • index: token order index in sentence
    • position: token chars span indices <included, excluded>
    • orth: word
    • lemma: lemmatised word
    • pos: part of speech
    • ctag: morphosyntactic tag
  • phrases: list of multi-word
  • wsd: annotation labels for the WSD task

Data Splits

We do not specify an exact data split for training and evaluation. However, we suggest to use GLEX and Składnica for training and other datasets for testing.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection, Normalization and Post-processing

Source corpora were initially pre-processed using morphosyntactic tagging and multi-word expression recognition tools. To tokenize and tag the datasets we used MorphoDiTa adapted to Polish language. To recognize multi-word expressions we applied pattern-based matching tool Corpus2-MWE - only MWEs from plWordNet were included. After manual annotation, sense indices of plWordNet 4.5 were mapped automatically to Princeton WordNet 3.0 and BabelNet 4.0 indices using plWordNet's interlingual mapping.

Annotations

Annotation process

  • 2+1 annotation process with inter-annotator agreement score over 0.6 PSA
  • annotated with plWordNet 4.5
  • software: WordNet-Loom and Inforex
  • both single-word and multi-word expressions annotated
  • full-text sense annotation (excluding KPWr)

Who are the annotators?

  • professional linguists (mention all people involved)

Personal and Sensitive Information

The datasets do not contain any personal or sensitive information.

Considerations for Using the Data

Discussion of Biases

Some datasets are biased towards most frequent senses. (Stats?)

Other Known Limitations

  • sense inventories are usually incomplete therefore some word senses might be missing in plWordNet
  • single-word and multi-word terms expressing novel senses (missing in plWordNet) were not marked

Additional Information

Dataset Curators

Arkadiusz Janz ([email protected])

Licensing Information

KPWR-100 CC-BY-SA 4.0
KPWR CC-BY-SA 4.0
Walenty CC-BY-SA 4.0
Sherlock CC-BY 4.0
Skladnica GNU GPL 3
GLEX plWordNet License

Citation Information

Main source (all corpora as a unified benchmark)

@InProceedings{10.1007/978-3-031-08754-7_70,
  author="Janz, Arkadiusz
  and Dziob, Agnieszka
  and Oleksy, Marcin
  and Baran, Joanna",
  editor="Groen, Derek
  and de Mulatier, Cl{\'e}llia
  and Paszynski, Maciej
  and Krzhizhanovskaya, Valeria V.
  and Dongarra, Jack J.
  and Sloot, Peter M. A.",
  title="A Unified Sense Inventory for Word Sense Disambiguation in Polish",
  booktitle="Computational Science -- ICCS 2022",
  year="2022",
  publisher="Springer International Publishing",
  address="Cham",
  pages="682--689",
  isbn="978-3-031-08754-7"
}

Related work

KPWr-100, Składnica, SPEC

@article{janzresults,
  title={Results of the PolEval 2020 Shared Task 3: Word Sense Disambiguation},
  author={Janz, Arkadiusz and Chlebus, Joanna and Dziob, Agnieszka and Piasecki, Maciej},
  journal={Proceedings of the PolEval 2020 Workshop},
  pages={65--77},
  year={2020}
}

GLEX (EmoGLEX)

@article{janz2017plwordnet,
  title={{plWordNet} as a basis for large emotive lexicons of Polish},
  author={Janz, Arkadiusz and Kocon, Jan and Piasecki, Maciej and Zasko-Zielinska, Monika},
  journal={Proceedings of Human Language Technologies as a Challenge for Computer Science and Linguistics Poznan: Fundacja Uniwersytetu im. Adama Mickiewicza w Poznaniu},
  pages={189--193},
  year={2017}
}

KPWr

@conference{broda2012,
    address = {Istanbul, Turkey},
    author = {Bartosz Broda and Micha{\l} Marci{\'n}czuk and Marek Maziarz and Adam Radziszewski and Adam Wardy{\'n}ski},
    booktitle = {Proceedings of LREC'12},
    owner = {Marlena},
    publisher = {ELRA},
    timestamp = {2014.06.20},
    title = {KPWr: Towards a Free Corpus of Polish},
    year = {2012}
}

Składnica

@inproceedings{hajnicz-2014-lexico,
    title = "Lexico-Semantic Annotation of Sk{\l}adnica Treebank by means of {PLWN} Lexical Units",
    author = "Hajnicz, El{\.z}bieta",
    booktitle = "Proceedings of the Seventh Global {W}ordnet Conference",
    month = jan,
    year = "2014",
    address = "Tartu, Estonia",
    publisher = "University of Tartu Press",
    url = "https://aclanthology.org/W14-0104",
    pages = "23--31",
}

Walenty

@inproceedings{haj:and:bar:lrec16,
    author = {Hajnicz, El{\.z}bieta and Andrzejczuk, Anna and Bartosiak, Tomasz},
    crossref = {lrec:16},
    pages = {2625--2632},
    pdf = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/382_Paper.pdf},
    title = {Semantic Layer of the Valence Dictionary of {P}olish \emph{{W}alenty}}
}