TestWUG EN: Test Word Usage Graphs for English Article Swipe
This data collection contains test Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the WUGsite. The data is provided for testing purposes and thus contains specific data cases, which are sometimes artificially created, sometimes picked from existing data sets. The data contains the following cases: afternoon_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 427 judgments. Has clear cluster structure with only one cluster, no graded change, no binary change, and medium agreement of 0.62 Krippendorff's alpha. arm: standard textbook example for semantic proximity (see reference below). Fully connected graph with six words uses, annotated by author. plane_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 1152 judgments. Has clear cluster structure, high graded change, binary change, and high agreement of 0.82 Krippendorff's alpha. target: similar to arm, but with only three repeated sentences. Fully connected graph with 8 word uses, annotated by author. Same sentence (exactly same string) is annotated with 4, different string is annotated with 1. Please find more information in the paper referenced below. Version: 1.2.0, 30.06.2023. Remove instances files as these should be inferred from judgments when aggregating. Reference Dominik Schlechtweg. 2023. Human and Computational Measurement of Lexical Semantic Change. PhD thesis. University of Stuttgart.
Related Topics
Concepts
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.7900959
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393530490
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393530490Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.7900959Digital Object Identifier
- Title
-
TestWUG EN: Test Word Usage Graphs for EnglishWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-30Full publication date if available
- Authors
-
Dominik SchlechtwegList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.7900959Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.7900959Direct OA link when available
- Concepts
-
Word (group theory), Test (biology), Computer science, Natural language processing, Linguistics, Philosophy, Biology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4393530490 |
|---|---|
| doi | https://doi.org/10.5281/zenodo.7900959 |
| ids.doi | https://doi.org/10.5281/zenodo.7900959 |
| ids.openalex | https://openalex.org/W4393530490 |
| fwci | |
| type | dataset |
| title | TestWUG EN: Test Word Usage Graphs for English |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10181 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9986000061035156 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Natural Language Processing Techniques |
| topics[1].id | https://openalex.org/T10028 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9937999844551086 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Topic Modeling |
| topics[2].id | https://openalex.org/T10743 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9699000120162964 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1712 |
| topics[2].subfield.display_name | Software |
| topics[2].display_name | Software Testing and Debugging Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C90805587 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6509228944778442 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q10944557 |
| concepts[0].display_name | Word (group theory) |
| concepts[1].id | https://openalex.org/C2777267654 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5802795886993408 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3519023 |
| concepts[1].display_name | Test (biology) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5382786393165588 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C204321447 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4357925057411194 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[3].display_name | Natural language processing |
| concepts[4].id | https://openalex.org/C41895202 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3154735565185547 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[4].display_name | Linguistics |
| concepts[5].id | https://openalex.org/C138885662 |
| concepts[5].level | 0 |
| concepts[5].score | 0.09190046787261963 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[5].display_name | Philosophy |
| concepts[6].id | https://openalex.org/C86803240 |
| concepts[6].level | 0 |
| concepts[6].score | 0.05896085500717163 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[6].display_name | Biology |
| concepts[7].id | https://openalex.org/C151730666 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[7].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/word |
| keywords[0].score | 0.6509228944778442 |
| keywords[0].display_name | Word (group theory) |
| keywords[1].id | https://openalex.org/keywords/test |
| keywords[1].score | 0.5802795886993408 |
| keywords[1].display_name | Test (biology) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5382786393165588 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/natural-language-processing |
| keywords[3].score | 0.4357925057411194 |
| keywords[3].display_name | Natural language processing |
| keywords[4].id | https://openalex.org/keywords/linguistics |
| keywords[4].score | 0.3154735565185547 |
| keywords[4].display_name | Linguistics |
| keywords[5].id | https://openalex.org/keywords/philosophy |
| keywords[5].score | 0.09190046787261963 |
| keywords[5].display_name | Philosophy |
| keywords[6].id | https://openalex.org/keywords/biology |
| keywords[6].score | 0.05896085500717163 |
| keywords[6].display_name | Biology |
| language | en |
| locations[0].id | doi:10.5281/zenodo.7900959 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400562 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[0].source.host_organization | https://openalex.org/I67311998 |
| locations[0].source.host_organization_name | European Organization for Nuclear Research |
| locations[0].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | dataset |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5281/zenodo.7900959 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5013366042 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0685-2576 |
| authorships[0].author.display_name | Dominik Schlechtweg |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I100066346 |
| authorships[0].affiliations[0].raw_affiliation_string | University of Stuttgart |
| authorships[0].institutions[0].id | https://openalex.org/I100066346 |
| authorships[0].institutions[0].ror | https://ror.org/04vnq7t77 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I100066346 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | University of Stuttgart |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dominik Schlechtweg |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | University of Stuttgart |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5281/zenodo.7900959 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | TestWUG EN: Test Word Usage Graphs for English |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10181 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9986000061035156 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Natural Language Processing Techniques |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W2382290278, https://openalex.org/W2478288626, https://openalex.org/W4391913857, https://openalex.org/W2350741829, https://openalex.org/W2296205523 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5281/zenodo.7900959 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400562 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| best_oa_location.source.host_organization | https://openalex.org/I67311998 |
| best_oa_location.source.host_organization_name | European Organization for Nuclear Research |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | dataset |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5281/zenodo.7900959 |
| primary_location.id | doi:10.5281/zenodo.7900959 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400562 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| primary_location.source.host_organization | https://openalex.org/I67311998 |
| primary_location.source.host_organization_name | European Organization for Nuclear Research |
| primary_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | dataset |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5281/zenodo.7900959 |
| publication_date | 2023-06-30 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.8 | 162 |
| abstract_inverted_index.a | 12 |
| abstract_inverted_index.1. | 182 |
| abstract_inverted_index.4, | 176 |
| abstract_inverted_index.EN | 63, 120 |
| abstract_inverted_index.as | 198 |
| abstract_inverted_index.be | 201 |
| abstract_inverted_index.by | 69, 114, 126, 166 |
| abstract_inverted_index.in | 187 |
| abstract_inverted_index.is | 31, 173, 179 |
| abstract_inverted_index.no | 83, 86 |
| abstract_inverted_index.of | 14, 92, 144, 215, 222 |
| abstract_inverted_index.on | 26 |
| abstract_inverted_index.to | 19, 150 |
| abstract_inverted_index.200 | 65, 122 |
| abstract_inverted_index.427 | 73 |
| abstract_inverted_index.Has | 75, 132 |
| abstract_inverted_index.PhD | 219 |
| abstract_inverted_index.The | 29, 53 |
| abstract_inverted_index.and | 23, 36, 89, 141, 212 |
| abstract_inverted_index.are | 43 |
| abstract_inverted_index.but | 152 |
| abstract_inverted_index.for | 9, 33, 100 |
| abstract_inverted_index.one | 81 |
| abstract_inverted_index.six | 110 |
| abstract_inverted_index.the | 15, 21, 27, 56, 188 |
| abstract_inverted_index.(see | 103 |
| abstract_inverted_index.0.62 | 93 |
| abstract_inverted_index.0.82 | 145 |
| abstract_inverted_index.1152 | 130 |
| abstract_inverted_index.DWUG | 62, 119 |
| abstract_inverted_index.Find | 11 |
| abstract_inverted_index.Same | 168 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.Word | 5 |
| abstract_inverted_index.arm, | 151 |
| abstract_inverted_index.arm: | 96 |
| abstract_inverted_index.code | 18 |
| abstract_inverted_index.data | 1, 16, 22, 30, 40, 51, 54 |
| abstract_inverted_index.find | 184 |
| abstract_inverted_index.from | 49, 61, 118, 203 |
| abstract_inverted_index.high | 136, 142 |
| abstract_inverted_index.more | 185 |
| abstract_inverted_index.only | 80, 154 |
| abstract_inverted_index.same | 171 |
| abstract_inverted_index.test | 4 |
| abstract_inverted_index.thus | 37 |
| abstract_inverted_index.uses | 66, 123 |
| abstract_inverted_index.when | 205 |
| abstract_inverted_index.with | 72, 79, 109, 129, 153, 161, 175, 181 |
| abstract_inverted_index.word | 163 |
| abstract_inverted_index.2023. | 210 |
| abstract_inverted_index.Fully | 106, 158 |
| abstract_inverted_index.Human | 211 |
| abstract_inverted_index.Usage | 6 |
| abstract_inverted_index.clear | 76, 133 |
| abstract_inverted_index.files | 197 |
| abstract_inverted_index.graph | 108, 160 |
| abstract_inverted_index.paper | 189 |
| abstract_inverted_index.sets. | 52 |
| abstract_inverted_index.these | 199 |
| abstract_inverted_index.three | 155 |
| abstract_inverted_index.uses, | 112, 164 |
| abstract_inverted_index.which | 42 |
| abstract_inverted_index.words | 111 |
| abstract_inverted_index.(WUGs) | 8 |
| abstract_inverted_index.1.2.0, | 193 |
| abstract_inverted_index.2.0.1. | 64, 121 |
| abstract_inverted_index.Graphs | 7 |
| abstract_inverted_index.Please | 183 |
| abstract_inverted_index.Remove | 195 |
| abstract_inverted_index.alpha. | 95, 147 |
| abstract_inverted_index.below. | 191 |
| abstract_inverted_index.binary | 87, 139 |
| abstract_inverted_index.cases, | 41 |
| abstract_inverted_index.cases: | 58 |
| abstract_inverted_index.graded | 84, 137 |
| abstract_inverted_index.medium | 90 |
| abstract_inverted_index.partly | 67, 124 |
| abstract_inverted_index.picked | 48 |
| abstract_inverted_index.should | 200 |
| abstract_inverted_index.string | 178 |
| abstract_inverted_index.Change. | 218 |
| abstract_inverted_index.Dominik | 208 |
| abstract_inverted_index.Lexical | 216 |
| abstract_inverted_index.author. | 115, 167 |
| abstract_inverted_index.below). | 105 |
| abstract_inverted_index.change, | 85, 88, 138, 140 |
| abstract_inverted_index.cluster | 77, 134 |
| abstract_inverted_index.example | 99 |
| abstract_inverted_index.format, | 17 |
| abstract_inverted_index.further | 24 |
| abstract_inverted_index.process | 20 |
| abstract_inverted_index.sampled | 60, 117 |
| abstract_inverted_index.similar | 149 |
| abstract_inverted_index.string) | 172 |
| abstract_inverted_index.target: | 148 |
| abstract_inverted_index.testing | 34 |
| abstract_inverted_index.thesis. | 220 |
| abstract_inverted_index.(exactly | 170 |
| abstract_inverted_index.English. | 10 |
| abstract_inverted_index.Semantic | 217 |
| abstract_inverted_index.Version: | 192 |
| abstract_inverted_index.WUGsite. | 28 |
| abstract_inverted_index.cluster, | 82 |
| abstract_inverted_index.contains | 3, 38, 55 |
| abstract_inverted_index.created, | 46 |
| abstract_inverted_index.datasets | 25 |
| abstract_inverted_index.existing | 50 |
| abstract_inverted_index.inferred | 202 |
| abstract_inverted_index.multiple | 70, 127 |
| abstract_inverted_index.provided | 32 |
| abstract_inverted_index.purposes | 35 |
| abstract_inverted_index.repeated | 156 |
| abstract_inverted_index.semantic | 101 |
| abstract_inverted_index.sentence | 169 |
| abstract_inverted_index.specific | 39 |
| abstract_inverted_index.standard | 97 |
| abstract_inverted_index.textbook | 98 |
| abstract_inverted_index.Reference | 207 |
| abstract_inverted_index.agreement | 91, 143 |
| abstract_inverted_index.annotated | 68, 113, 125, 165, 174, 180 |
| abstract_inverted_index.connected | 107, 159 |
| abstract_inverted_index.different | 177 |
| abstract_inverted_index.following | 57 |
| abstract_inverted_index.instances | 196 |
| abstract_inverted_index.judgments | 204 |
| abstract_inverted_index.plane_nn: | 116 |
| abstract_inverted_index.proximity | 102 |
| abstract_inverted_index.reference | 104 |
| abstract_inverted_index.sometimes | 44, 47 |
| abstract_inverted_index.structure | 78 |
| abstract_inverted_index.Stuttgart. | 223 |
| abstract_inverted_index.University | 221 |
| abstract_inverted_index.annotators | 71, 128 |
| abstract_inverted_index.collection | 2 |
| abstract_inverted_index.judgments. | 74, 131 |
| abstract_inverted_index.referenced | 190 |
| abstract_inverted_index.sentences. | 157 |
| abstract_inverted_index.structure, | 135 |
| abstract_inverted_index.30.06.2023. | 194 |
| abstract_inverted_index.Measurement | 214 |
| abstract_inverted_index.description | 13 |
| abstract_inverted_index.information | 186 |
| abstract_inverted_index.Schlechtweg. | 209 |
| abstract_inverted_index.aggregating. | 206 |
| abstract_inverted_index.artificially | 45 |
| abstract_inverted_index.Computational | 213 |
| abstract_inverted_index.afternoon_nn: | 59 |
| abstract_inverted_index.Krippendorff's | 94, 146 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5013366042 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I100066346 |
| citation_normalized_percentile |