Name
Abstract | ||
|---|---|---|
I review the problem of referential ambiguity that arises when children learn the meanings of words, along with a number of models that have been proposed to solve it. I then provide a formal analysis of why a resource-limited model that retains very few meaning hypotheses may be more effective than "big data" models that keep track of all word-meaning associations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1111/tops.12415 | TOPICS IN COGNITIVE SCIENCE |
Keywords | DocType | Volume |
Word learning,Mathematical modeling,Cross-situational learning,Language acquisition | Journal | 12.0 |
Issue | ISSN | Citations |
SP1.0 | 1756-8757 | 0 |
PageRank | References | Authors |
0.34 | 4 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charles D. Yang | 1 | 5 | 5.14 |