CTSL-LEX

CTSL-LEX is a database of lexical and phonological properties of nearly 1,000 signs in American Sign Language. This project is a collaboration between the Laboratory for Language and Cognitive Neuroscience at San Diego State University and the Psycholinguistics and Linguistics Lab at Tufts University. CTSL-LEX is a searchable database of subjective frequency ratings, iconicity ratings, lexical properties (e.g., initialized signs; lexical class), and six phonological features from which neighborhood densities have been calculated. CTSL-LEX also provides, reference video clips, English translations and, for a subset of signs, alternative translations.

Basic keyword search

Type the English translation in the search box and press enter. Information about that sign will be available in the “Sign Data” tab, and the sign node in the visual lexicon will move to the center of the screen. You can also click "About this Data" for more information on the data that is displayed. If more than one sign or English translation matches what you have typed, you will see a list of words to select from.

Advanced search – using filters

You can narrow down the lexicon by applying filters (e.g., requesting a range of frequency or iconicity values) on the “Filter” tab.

Downloading data

To download data, select the download button at the top right corner of the visualization. Select the properties that you would like to download.

To get in contact, please feel free to e-mail us.

Many thanks to Cindy O'Grady Farnady for modeling all of the signs and helping with data collection, to Ben Tanen for his help developing this website, and to Jennifer Feeney Petrich for her help conceptualizing and establishing the project. Thanks also to Marissa Maimone, Yvonne Xiong, Rebecca Nardulli, Adriana Alcala, Christina Massrey, Melissa Watton, Caitlyn Lahousse, Christina Ng, Jessica Mow, Laura Coughlin, and Taylor Strelevitz for their help coding lexical and phonological properties, and to Jordan Weber and Mahie Sumathipala for help preparing materials and coding. This work is supported by the National Institutes of Health DC010997 to Dr. Karen Emmorey and San Diego State University, and a Tufts University Faculty Research Award to Dr. Ariel M. Cohen-Goldberg.