Name
Abstract | ||
|---|---|---|
A key challenge faced by children in vocabulary acquisition is learning which of the many possible meanings is appropriate for a word. The word generalization problem refers to how children associate a word such as dog with a meaning at the appropriate category level in the taxonomy of objects, such as Dalmatians, dogs, or animals. We present extensions to a cross-situational learner that enable the first computational study of word generalization integrated within a word learning model. The model simulates child patterns of word generalization due to the interaction of type and token frequencies in the input data, an influence often observed in usage-based approaches to underlie people’s generalization of linguistic categories. |
Year | Venue | Field |
|---|---|---|
2015 | CogSci | Psychology,Artificial intelligence,Natural language processing,Word learning,Vocabulary,Security token |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aida Nematzadeh | 1 | 25 | 9.37 |
| Erin Grant | 2 | 25 | 5.71 |
| Suzanne Stevenson | 3 | 566 | 64.31 |