Liat Nativ
Biographic Data
| ID | 8261706 |
|---|---|
| NAME | Liat Nativ |
| GIVEN NAMES | Liat |
| FAMILY NAME | Nativ |
| SIGNATURE | NATIV L |
| AFFILIATIONS | University of Haifa |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Do more proficient writers use fewer cognates in L2? A computational approach
Bilinguals often show evidence of cross language influences, such as facilitation in processing cognates. Here we use computational methods for analyzing spontaneous English texts written by hundreds of speakers of different L1s, at different levels of English proficiency, to investigate writers’ preference for using cognates over alternative word choices. We focus on English, since a majority of its lexicon is either of Romance or Germanic origi…
A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning
Early detection of autism spectrum disorder (ASD) is crucial for timely intervention, yet diagnosis typically occurs after age three. This study aimed to develop a machine learning model to predict ASD diagnosis using infants' electronic health records obtained through a national screening program and evaluate its accuracy. A retrospective cohort study analyzed health records of 780,610 children, including 1163 with ASD diagnoses. Data encompasse…
No prominent works on this page.
Do more proficient writers use fewer cognates in L2? A computational approach
Bilinguals often show evidence of cross language influences, such as facilitation in processing cognates. Here we use computational methods for analyzing spontaneous English texts written by hundreds of speakers of different L1s, at different levels of English proficiency, to investigate writers’ preference for using cognates over alternative word choices. We focus on English, since a majority of its lexicon is either of Romance or Germanic origi…
A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning
Early detection of autism spectrum disorder (ASD) is crucial for timely intervention, yet diagnosis typically occurs after age three. This study aimed to develop a machine learning model to predict ASD diagnosis using infants' electronic health records obtained through a national screening program and evaluate its accuracy. A retrospective cohort study analyzed health records of 780,610 children, including 1163 with ASD diagnoses. Data encompasse…
Artificial Intelligence (2 works) · Computer Science (2 works) · Psychology (2 works) · Argumentative (1 work) · Autism (1 work) · Autism spectrum disorder (1 work) · Autism Spectrum Disorder Research (1 work) · Child Development and Digital Technology (1 work) · Developmental psychology (1 work) · German (1 work)