Concept information
Preferred term
linguistic category
Broader concept
Narrower concepts
Example
- Each of the layers is specified by its relevant linguistic category (stem paradigm case number gender etc.) and possibly by an error label. (Rosen, 2016)
- The fact that some linguistic categories have a steeper curve than the others may also signalise the difficulty of these categories from a machine learning perspective. (Stadler, Macketanz & Avramidis, 2021)
- To study whether different linguistic categories are jointly encoded within LLMs we use a crossneutralization method. (Starace, Papakostas, Choenni, Panagiotopoulos, Rosati, Leidinger & Shutova, 2023)
- We study information sharing between linguistic categories in LLMs finding evidence of joint encoding between pairs of related POS tag classes. (Starace, Papakostas, Choenni, Panagiotopoulos, Rosati, Leidinger & Shutova, 2023)
- We test this hypothesis by cross-neutralizing every linguistic category in a language A with another category from a language B e.g. neutralizing all Italian POS tags with English nouns. (Starace, Papakostas, Choenni, Panagiotopoulos, Rosati, Leidinger & Shutova, 2023)
In other languages
-
French
URI
http://data.loterre.fr/ark:/67375/8LP-WFW61X72-6
{{label}}
{{#each values }} {{! loop through ConceptPropertyValue objects }}
{{#if prefLabel }}
{{/if}}
{{/each}}
{{#if notation }}{{ notation }} {{/if}}{{ prefLabel }}
{{#ifDifferentLabelLang lang }} ({{ lang }}){{/ifDifferentLabelLang}}
{{#if vocabName }}
{{ vocabName }}
{{/if}}