: Ordinal (53A) and distributive (54A) numerals, and numeral classifiers (55A). Nominal Syntax (Chapters 58–64) :
: Perfective/imperfective aspect (65A), past tense (66A), future tense (67A), and the perfect (68A).
: Position of tense-aspect affixes (69A) and the morphological imperative (70A). Use Cases for the Dataset WALS roberta sets 37-70.zip
: Leveraging the broad cross-linguistic data in WALS to improve how models handle the hundreds of languages that lack large amounts of training text.
The features in this range are essential for understanding how different languages handle noun and verb structures. : : Ordinal (53A) and distributive (54A) numerals, and
World languages with features and coordinates - Dataset Search
: Noun phrase conjunction (63A) versus verbal conjunction (64A). Verbal Categories (Chapters 65–70) : Use Cases for the Dataset : Leveraging the
: Using the WALS database features as labels to see if a model's internal representations (embeddings) cluster according to known linguistic traits, such as whether a language uses definite articles.
: Ordinal (53A) and distributive (54A) numerals, and numeral classifiers (55A). Nominal Syntax (Chapters 58–64) :
: Perfective/imperfective aspect (65A), past tense (66A), future tense (67A), and the perfect (68A).
: Position of tense-aspect affixes (69A) and the morphological imperative (70A). Use Cases for the Dataset
: Leveraging the broad cross-linguistic data in WALS to improve how models handle the hundreds of languages that lack large amounts of training text.
The features in this range are essential for understanding how different languages handle noun and verb structures. :
World languages with features and coordinates - Dataset Search
: Noun phrase conjunction (63A) versus verbal conjunction (64A). Verbal Categories (Chapters 65–70) :
: Using the WALS database features as labels to see if a model's internal representations (embeddings) cluster according to known linguistic traits, such as whether a language uses definite articles.