Main content

Home

Menu

View
Wiki Version:
Machine learning can identify, with reasonable accuracy, the native language of someone writing in a foreign language (Joel Tetreault et al., 2013). Intriguingly, native language identification (NLI) can be accomplished looking only at the syntactic structure, ignoring word choice (Swanson, 2013). This finding has potentially broad relevance to cognitive science since it suggests a broad-based method to empirically study the effects of first language syntax on second language (L1->L2 transfer). However, that requires interpretation of the resulting models, which is notoriously difficult (Williams et al., 2017). As a first step, we compare the results of a variety of state-of-the-art machine learning techniques on NLI in two languages: English and Spanish. Join our virtual poster presentation! Poster Session A | 3/19/2020 12pm - 2pm https://mit.zoom.us/j/913445038?
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.

Copyright © 2011-2024 Center for Open Science | Terms of Use | Privacy Policy | Status | API
TOP Guidelines | Reproducibility Project: Psychology | Reproducibility Project: Cancer Biology