Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection Article Swipe
Dominik Schlechtweg
,
Frank D. Zamora-Reina
,
Felipe Bravo-Márquez
,
Nikolay Arefyev
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10579-024-09771-7
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10579-024-09771-7
There has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10579-024-09771-7
- OA Status
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- References
- 77
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4402768365Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10579-024-09771-7Digital Object Identifier
- Title
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Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-09-20Full publication date if available
- Authors
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Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Márquez, Nikolay ArefyevList of authors in order
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https://doi.org/10.1007/s10579-024-09771-7Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1007/s10579-024-09771-7Direct OA link when available
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Word (group theory), Semantic change, Sense (electronics), Word-sense disambiguation, SemEval, Linguistics, Computer science, Natural language processing, Artificial intelligence, Philosophy, Management, Economics, Electrical engineering, Task (project management), EngineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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77Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.and | 36, 63, 104, 106, 145, 147, 166, 182, 232 |
| abstract_inverted_index.are | 49, 172, 224 |
| abstract_inverted_index.for | 28, 124, 161, 204 |
| abstract_inverted_index.has | 2 |
| abstract_inverted_index.new | 133 |
| abstract_inverted_index.not | 66 |
| abstract_inverted_index.our | 170 |
| abstract_inverted_index.the | 29, 68, 72, 82, 111, 143, 195, 209, 213, 236 |
| abstract_inverted_index.LSCD | 167, 210, 237 |
| abstract_inverted_index.Such | 23 |
| abstract_inverted_index.This | 74, 218 |
| abstract_inverted_index.Word | 32, 37 |
| abstract_inverted_index.able | 233 |
| abstract_inverted_index.been | 3 |
| abstract_inverted_index.both | 164, 205, 222 |
| abstract_inverted_index.date | 70 |
| abstract_inverted_index.even | 207 |
| abstract_inverted_index.into | 101 |
| abstract_inverted_index.more | 230 |
| abstract_inverted_index.need | 123 |
| abstract_inverted_index.task | 112, 197, 211 |
| abstract_inverted_index.that | 178, 193, 220 |
| abstract_inverted_index.then | 152 |
| abstract_inverted_index.time | 62 |
| abstract_inverted_index.uses | 15 |
| abstract_inverted_index.were | 25 |
| abstract_inverted_index.when | 208 |
| abstract_inverted_index.with | 46, 110, 186 |
| abstract_inverted_index.word | 8, 14, 47, 90, 126 |
| abstract_inverted_index.work | 5 |
| abstract_inverted_index.(WSD) | 35 |
| abstract_inverted_index.Sense | 33, 38 |
| abstract_inverted_index.There | 1 |
| abstract_inverted_index.based | 173 |
| abstract_inverted_index.data. | 128 |
| abstract_inverted_index.focal | 214 |
| abstract_inverted_index.graph | 183 |
| abstract_inverted_index.human | 7 |
| abstract_inverted_index.i.e., | 11, 52 |
| abstract_inverted_index.often | 64 |
| abstract_inverted_index.point | 215 |
| abstract_inverted_index.seems | 227 |
| abstract_inverted_index.sense | 9 |
| abstract_inverted_index.short | 59 |
| abstract_inverted_index.task. | 238 |
| abstract_inverted_index.tasks | 30, 206, 223 |
| abstract_inverted_index.texts | 18, 54 |
| abstract_inverted_index.their | 21 |
| abstract_inverted_index.there | 119 |
| abstract_inverted_index.time. | 97 |
| abstract_inverted_index.using | 194 |
| abstract_inverted_index.where | 81 |
| abstract_inverted_index.(WSI). | 40 |
| abstract_inverted_index.Change | 116 |
| abstract_inverted_index.Hence, | 129 |
| abstract_inverted_index.across | 96 |
| abstract_inverted_index.almost | 42 |
| abstract_inverted_index.better | 202 |
| abstract_inverted_index.induce | 86 |
| abstract_inverted_index.labels | 24 |
| abstract_inverted_index.models | 181 |
| abstract_inverted_index.period | 60 |
| abstract_inverted_index.reveal | 192 |
| abstract_inverted_index.senses | 48, 91, 95 |
| abstract_inverted_index.tasks. | 168 |
| abstract_inverted_index.texts. | 73 |
| abstract_inverted_index.yields | 201 |
| abstract_inverted_index.(LSCD), | 118 |
| abstract_inverted_index.Lexical | 114 |
| abstract_inverted_index.WSD/WSI | 136 |
| abstract_inverted_index.analyze | 148 |
| abstract_inverted_index.central | 149 |
| abstract_inverted_index.changes | 93 |
| abstract_inverted_index.contain | 53 |
| abstract_inverted_index.created | 27, 55, 131 |
| abstract_inverted_index.crucial | 122 |
| abstract_inverted_index.dataset | 137 |
| abstract_inverted_index.general | 231 |
| abstract_inverted_index.ignores | 75 |
| abstract_inverted_index.jointly | 162 |
| abstract_inverted_index.natural | 17 |
| abstract_inverted_index.provide | 67 |
| abstract_inverted_index.results | 203 |
| abstract_inverted_index.senses. | 22 |
| abstract_inverted_index.solving | 163 |
| abstract_inverted_index.systems | 160, 171 |
| abstract_inverted_index.‘DWUG | 138 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.Semantic | 115 |
| abstract_inverted_index.although | 221 |
| abstract_inverted_index.combines | 179 |
| abstract_inverted_index.creation | 69 |
| abstract_inverted_index.datasets | 44 |
| abstract_inverted_index.describe | 142, 153 |
| abstract_inverted_index.findings | 191 |
| abstract_inverted_index.labeling | 13 |
| abstract_inverted_index.manually | 12 |
| abstract_inverted_index.possible | 76 |
| abstract_inverted_index.related, | 225 |
| abstract_inverted_index.reliable | 134 |
| abstract_inverted_index.thorough | 155 |
| abstract_inverted_index.Detection | 117 |
| abstract_inverted_index.Induction | 39 |
| abstract_inverted_index.Sense’. | 140 |
| abstract_inverted_index.according | 19 |
| abstract_inverted_index.annotated | 45 |
| abstract_inverted_index.criterion | 200 |
| abstract_inverted_index.datasets, | 51 |
| abstract_inverted_index.different | 158, 187 |
| abstract_inverted_index.establish | 108 |
| abstract_inverted_index.extensive | 4 |
| abstract_inverted_index.primarily | 26 |
| abstract_inverted_index.settings, | 80 |
| abstract_inverted_index.settings. | 189 |
| abstract_inverted_index.annotation | 146 |
| abstract_inverted_index.clustering | 184 |
| abstract_inverted_index.diachronic | 135 |
| abstract_inverted_index.evaluation | 156 |
| abstract_inverted_index.facilitate | 99 |
| abstract_inverted_index.historical | 89, 102, 125 |
| abstract_inverted_index.prediction | 159 |
| abstract_inverted_index.relatively | 58 |
| abstract_inverted_index.synchronic | 50 |
| abstract_inverted_index.techniques | 185 |
| abstract_inverted_index.annotation, | 10 |
| abstract_inverted_index.connections | 109 |
| abstract_inverted_index.incorporate | 235 |
| abstract_inverted_index.preparation | 144 |
| abstract_inverted_index.statistics. | 150 |
| abstract_inverted_index.underscores | 219 |
| abstract_inverted_index.applications | 77 |
| abstract_inverted_index.architecture | 177 |
| abstract_inverted_index.disambiguate | 88 |
| abstract_inverted_index.optimization | 199 |
| abstract_inverted_index.optimization. | 217 |
| abstract_inverted_index.Disambiguation | 34 |
| abstract_inverted_index.hyperparameter | 188 |
| abstract_inverted_index.investigations | 100 |
| abstract_inverted_index.Word-in-Context | 180 |
| abstract_inverted_index.sense-annotated | 127 |
| abstract_inverted_index.state-of-the-art | 176 |
| abstract_inverted_index.diachronic-historic | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.17184089 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |