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Found in: ključnih besedah
Keywords: predictive economies, data, social tech, machine learning, autonomy, data worker, trade union, solidarity, labour extraction, data labour rights
Published: 08.12.2020; Views: 1642; Downloads: 19
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Abstract: How does linguistic structure affect children’s acquisition of early number word meanings? Previous studies have tested this question by comparing how children learning languages with different grammatical representations of number learn the meanings of labels for small numbers, like 1, 2, and 3. For example, children who acquire a language with singular-plural marking, like English, are faster to learn the word for 1 than children learning a language that lacks the singular-plural distinction, perhaps because the word for 1 is always used in singular contexts, highlighting its meaning. These studies are problematic, however, because reported differences in number word learning may be due to unmeasured cross-cultural differences rather than specific linguistic differences. To address this problem, we investigated number word learning in four groups of children from a single culture who spoke different dialects of the same language that differed chiefly with respect to how they grammatically mark number. We found that learning a dialect which features “dual” morphology (marking of pairs) accelerated children’s acquisition of the number word two relative to learning a “non-dual” dialect of the same language.
Found in: ključnih besedah
Keywords: števila, številke, slovnično število, dvojina, narečja, usvajanje, učenje, slovenščina, angleščina, numbers, grammatical number, dual, dialects, acquisition, learning, Slovenian, English
Published: 10.08.2016; Views: 4087; Downloads: 233
Fulltext (1,38 MB)

Abstract: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Found in: ključnih besedah
Keywords: Machine learning, Feature construction, Rule learning, Unknown attribute values
Published: 14.07.2017; Views: 3587; Downloads: 0
Fulltext (365,76 KB)

Found in: ključnih besedah
Keywords: CTA, event classification, deep learning
Published: 16.02.2018; Views: 2733; Downloads: 135
Fulltext (313,07 KB)

Found in: ključnih besedah
Keywords: Second language learning, ultimate attainment, syntax, sentence processing, negative evidence
Published: 18.01.2019; Views: 2735; Downloads: 0
Fulltext (205,89 KB)