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}
] | [] | indigenous plants and animals | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00019128 03 n 01 natural_object 0 022 @ 00003553 n 0000 ! 00021939 n 0101 ~ 02312744 n 0000 ~ 05216365 n 0000 ~ 05217859 n 0000 ~ 09208496 n 0000 ~ 09222406 n 0000 ~ 09224911 n 0000 ~ 09236766 n 0000 ~ 09239740 n 0000 ~ 09252586 n 0000 ~ 09252970 n 0000 ~ 09257949 n 0000 ~ 09277686 n 0000 ~ 09349797 n 0000 ~ 09369169 n... | true | 00019128 | 00019128 | 03 | n | 1 | 1 | [
{
"lemma": "natural object",
"lex_id": "0",
"lex_id_int": 0,
"marker": null,
"word": "natural_object",
"word_number": 1
}
] | [
"natural object"
] | 22 | 22 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00003553",
"target_word_number": 0
},
{
"is_semantic": false,
"label": "antonym",
"pos": "n",
"source_target": "0101",
"s... | [] | an object occurring naturally; not made by man | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00019613 03 n 01 substance 0 019 @ 00020827 n 0000 @ 13809207 n 0000 + 00625393 a 0101 ~ 05263850 n 0000 ~ 05432736 n 0000 ~ 14580752 n 0000 ~ 14580897 n 0000 ~ 14583228 n 0000 ~ 14586258 n 0000 ~ 14619225 n 0000 ~ 14622893 n 0000 ~ 14723079 n 0000 ~ 14738892 n 0000 ~ 14840755 n 0000 ~ 14899152 n 0000 ~ 14899530 n 0000... | true | 00019613 | 00019613 | 03 | n | 1 | 1 | [
{
"lemma": "substance",
"lex_id": "0",
"lex_id_int": 0,
"marker": null,
"word": "substance",
"word_number": 1
}
] | [
"substance"
] | 19 | 19 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00020827",
"target_word_number": 0
},
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"s... | [] | the real physical matter of which a person or thing consists; "DNA is the substance of our genes" | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00020090 03 n 01 substance 1 032 @ 00020827 n 0000 ~ 00021265 n 0000 ~ 00021734 n 0000 ~ 14582870 n 0000 ~ 14584260 n 0000 ~ 14584390 n 0000 ~ 14584502 n 0000 ~ 14584658 n 0000 ~ 14590975 n 0000 ~ 14616939 n 0000 ~ 14691822 n 0000 ~ 14710024 n 0000 ~ 14724436 n 0000 ~ 14738752 n 0000 ~ 14778436 n 0000 ~ 14793812 n 0000... | true | 00020090 | 00020090 | 03 | n | 1 | 1 | [
{
"lemma": "substance",
"lex_id": "1",
"lex_id_int": 1,
"marker": null,
"word": "substance",
"word_number": 1
}
] | [
"substance"
] | 32 | 32 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00020827",
"target_word_number": 0
},
{
"is_semantic": true,
"label": "hyponym",
"pos": "n",
"source_target": "0000",
"so... | [] | a particular kind or species of matter with uniform properties; "shigella is one of the most toxic substances known to man" | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00020827 03 n 01 matter 0 016 @ 00001930 n 0000 ~ 00019613 n 0000 ~ 00020090 n 0000 ~ 09428967 n 0000 ~ 14582025 n 0000 ~ 14582220 n 0000 ~ 14582535 n 0000 ~ 14583573 n 0000 ~ 14939445 n 0000 ~ 14956661 n 0000 ~ 14981183 n 0000 ~ 15004501 n 0000 ~ 15046900 n 0000 ~ 15047849 n 0000 ~ 15109745 n 0000 ~ 15110956 n 0000 | ... | true | 00020827 | 00020827 | 03 | n | 1 | 1 | [
{
"lemma": "matter",
"lex_id": "0",
"lex_id_int": 0,
"marker": null,
"word": "matter",
"word_number": 1
}
] | [
"matter"
] | 16 | 16 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00001930",
"target_word_number": 0
},
{
"is_semantic": true,
"label": "hyponym",
"pos": "n",
"source_target": "0000",
"so... | [] | that which has mass and occupies space; "physicists study both the nature of matter and the forces which govern it" | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00021265 03 n 02 food 0 nutrient 0 019 @ 00020090 n 0000 + 02557719 a 0204 + 01204191 v 0201 ~ 01464844 n 0000 %p 07555863 n 0000 ~ 07556406 n 0000 ~ 07556637 n 0000 ~ 07560652 n 0000 ~ 07566340 n 0000 ~ 07570720 n 0000 ~ 07572353 n 0000 ~ 07800091 n 0000 ~ 07860629 n 0000 ~ 07881800 n 0000 ~ 07935504 n 0000 ~ 07938313... | true | 00021265 | 00021265 | 03 | n | 2 | 2 | [
{
"lemma": "food",
"lex_id": "0",
"lex_id_int": 0,
"marker": null,
"word": "food",
"word_number": 1
},
{
"lemma": "nutrient",
"lex_id": "0",
"lex_id_int": 0,
"marker": null,
"word": "nutrient",
"word_number": 2
}
] | [
"food",
"nutrient"
] | 19 | 19 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00020090",
"target_word_number": 0
},
{
"is_semantic": false,
"label": "derivationally_related_form",
"pos": "a",
"source_tar... | [] | any substance that can be metabolized by an animal to give energy and build tissue | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
00021734 03 n 01 nutrient 1 002 @ 00020090 n 0000 ~ 07935504 n 0000 | any substance (such as a chemical element or inorganic compound) that can be taken in by a green plant and used in organic synthesis | true | 00021734 | 00021734 | 03 | n | 1 | 1 | [
{
"lemma": "nutrient",
"lex_id": "1",
"lex_id_int": 1,
"marker": null,
"word": "nutrient",
"word_number": 1
}
] | [
"nutrient"
] | 2 | 2 | [
{
"is_semantic": true,
"label": "hypernym",
"pos": "n",
"source_target": "0000",
"source_word_number": 0,
"symbol": "@",
"target_offset": "00020090",
"target_word_number": 0
},
{
"is_semantic": true,
"label": "hyponym",
"pos": "n",
"source_target": "0000",
"so... | [] | any substance (such as a chemical element or inorganic compound) that can be taken in by a green plant and used in organic synthesis | false | null | null | null | null | [] | null | null | [] | null | null | [] | [] | data |
End of preview. Expand in Data Studio
WordNet 3.0
Overview
- dataset_id: wordnet
- license: WordNet 3.0 license (Princeton)
- homepage: https://wordnet.princeton.edu
Dataset Structure Guide
This dataset publishes multiple Hub configs so researchers can choose the right view for their workflow.
What Configs Mean
A config is a named dataset view. It selects which files are loaded and which split names are exposed.
Available Configs
- aggregate: Default aggregate view across available variant paths.
- splits: noun, verb, adj, adv
- data: Synset data files with glosses, words, pointers, and verb frames.
- splits: noun, verb, adj, adv
- file prefix: variants/data/
- index: Index files with lemma metadata, pointer symbol inventory, and synset offsets.
- splits: noun, verb, adj, adv
- file prefix: variants/index/
- exceptions: Exception files mapping inflected forms to base forms.
- splits: noun, verb, adj, adv
- file prefix: variants/exceptions/
Quick Start (datasets library)
Use the config explicitly to avoid ambiguity and to get the expected split layout:
from datasets import load_dataset
# Replace with your Hub repo id, e.g. '<namespace>/wordnet'
dataset = load_dataset('<namespace>/wordnet', 'aggregate')
print(dataset)
Split Semantics
These splits may represent domain structure (for example part-of-speech groups) rather than train/validation/test partitions. If you need ML evaluation splits, create an explicit derived split plan in downstream preprocessing.
Summary
WordNet is published as lexical-resource views rather than benchmark train/validation/test tasks.
Intended Use
- Lexical semantic analysis and symbolic NLP workflows.
- Knowledge graph and relation-structure exploration.
- Input resource for downstream task-specific data construction.
Schema Notes
- The same part-of-speech split names (noun, verb, adj, adv) can appear across multiple configs.
- The aggregate config combines variant outputs while preserving split names.
Evaluation Protocol
- For ML benchmarks, define explicit train/validation/test derivations from the lexical splits.
- Record any filtering or balancing logic to keep experiments reproducible.
Limitations
- Not a native benchmark dataset with pre-defined evaluation partitions.
- Coverage and lexical conventions follow WordNet source files.
Config Selection Guidance
- Use the data config for synset-level lexical structure.
- Use the index config for lemma and offset lookup behavior.
- Use the exceptions config for inflection-to-base mappings.
- Use aggregate when you want one entry point and can filter by variant in downstream code.
Standardization
- split_aliases: {"dev": "validation", "val": "validation", "valid": "validation"}
- rename_map: {}
- selected_columns: []
Exported Artifacts
- noun.parquet
- verb.parquet
- adj.parquet
- adv.parquet
- data
- WORDNET_FORMAT.md
- views/synsets.parquet
- views/words.parquet
- views/pointers.parquet
- views/frames.parquet
Provided
- context: {"repository_navigation": [{"active": false, "description": "Synset data files with glosses, words, pointers, and verb frames.", "path_in_repo": "variants/data", "splits": ["noun", "verb", "adj", "adv"], "variant": "data"}, {"active": false, "description": "Index files with lemma metadata, pointer symbol inventory, and synset offsets.", "path_in_repo": "variants/index", "splits": ["noun", "verb", "adj", "adv"], "variant": "index"}, {"active": false, "description": "Exception files mapping inflected forms to base forms.", "path_in_repo": "variants/exceptions", "splits": ["noun", "verb", "adj", "adv"], "variant": "exceptions"}], "schema": ["text", "is_record", "offset", "synset_offset", "lex_filenum", "ss_type", "w_cnt", "word_count", "words", "lemmas", "p_cnt", "pointer_count", "pointers", "frames", "gloss", "parse_error", "lemma", "lemma_text", "pos", "synset_cnt", "ptr_symbols", "sense_cnt", "tagsense_cnt", "synset_offsets", "inflected_form", "inflected_form_text", "base_forms", "base_forms_text", "variant"], "scope": "aggregate", "shared_description": "WordNet publishes an aggregate lexical view that keeps the noun, verb, adjective, and adverb splits intact while combining data, index, and exception rows under an explicit variant column.", "splits": ["noun", "verb", "adj", "adv"], "viewer_config_name": "aggregate"}
Repository Navigation
- data: variants/data | Synset data files with glosses, words, pointers, and verb frames.
- index: variants/index | Index files with lemma metadata, pointer symbol inventory, and synset offsets.
- exceptions: variants/exceptions | Exception files mapping inflected forms to base forms.
Citation
Miller (1995), WordNet: A Lexical Database for English.
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