Laurel Perkins
Biographic Data
| ID | 8832493 |
|---|---|
| NAME | Laurel Perkins |
| GIVEN NAMES | Laurel |
| FAMILY NAME | Perkins |
| SIGNATURE | PERKINS L |
| AFFILIATIONS | University of California, Los Angeles |
| ORCID | 0000-0003-0719-9510 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2018 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Modeling regularization in language acquisition as noise-tolerant grammar selection
Mind the gap
In acquiring a syntax, children must detect evidence for abstract structural dependencies that can be realized in variable ways in the surface forms of sentences. In What did David fix? , learners must identify a nonlocal relation between a fronted object of the verb ( what ) and the phonologically null ‘gap’ in canonical direct object position after the verb, where it is thematically interpreted. How do learners identify a nonadjacent dependency…
Thematic Content, Not Number Matching, Drives Syntactic Bootstrapping
Children use correlations between the syntax of a clause and the meaning of its predicate to draw inferences about word meanings. On one proposal, these inferences are underwritten by a structural similarity between syntactic and semantic representations: learners expect that the number of clause arguments exactly matches the number of participant roles in the event concept under which its referent is viewed. We argue against this proposal, and i…
Learning unaccusativity
We examine four features of unaccusativity in child-directed and child Spanish to determine what cues children might use to distinguish unaccusative and unergative verbs. Two are cross-linguistic lexico-semantic features: Subjects of unaccusatives are patients so we expect more inanimate subjects with unaccusatives; and unaccusatives tend to have an endpoint, hence may occur more frequently with perfective aspect. The other two are language-speci…
The importance of input representations
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The importance of input representations
Preview this article: The importance of input representations, Page 1 of 1 /docserver/preview/fulltext/lab.18051.lid-1.gif
Learning unaccusativity
We examine four features of unaccusativity in child-directed and child Spanish to determine what cues children might use to distinguish unaccusative and unergative verbs. Two are cross-linguistic lexico-semantic features: Subjects of unaccusatives are patients so we expect more inanimate subjects with unaccusatives; and unaccusatives tend to have an endpoint, hence may occur more frequently with perfective aspect. The other two are language-speci…
Thematic Content, Not Number Matching, Drives Syntactic Bootstrapping
Children use correlations between the syntax of a clause and the meaning of its predicate to draw inferences about word meanings. On one proposal, these inferences are underwritten by a structural similarity between syntactic and semantic representations: learners expect that the number of clause arguments exactly matches the number of participant roles in the event concept under which its referent is viewed. We argue against this proposal, and i…
Modeling regularization in language acquisition as noise-tolerant grammar selection
Mind the gap
In acquiring a syntax, children must detect evidence for abstract structural dependencies that can be realized in variable ways in the surface forms of sentences. In What did David fix? , learners must identify a nonlocal relation between a fronted object of the verb ( what ) and the phonologically null ‘gap’ in canonical direct object position after the verb, where it is thematically interpreted. How do learners identify a nonadjacent dependency…
Language Development and Disorders (4 works) · Computer Science (2 works) · Language and cultural evolution (2 works) · Natural Language Processing Techniques (2 works) · Neurobiology of Language and Bilingualism (2 works) · Psychology (2 works) · Reading and Literacy Development (2 works) · Syntax (2 works) · Argument (complex analysis) (1 work) · Artificial Intelligence (1 work)