Welcome to the Center for Research in Language (CRL)

CRL brings together faculty, students and research associates who share an interest in the nature of language, the processes by which language is acquired and used, and the mediation of language in the human brain.

CRL is housed in the Cognitive Science Building on the Thurgood Marshall Campus at the University of California, San Diego and boasts an interdisciplinary academic staff comprised of specialists in a wide variety of fields:

  • Cognitive science
  • Communication
  • Communication disorders
  • Computer science
  • Developmental psychology
  • Linguistics
  • Neurosciences
  • Pediatrics
  • Psycholinguistics

CRL Talks

February 23

Mapping the semantic structure of American Sign Language lexicon using the ASL-LEX Database

Zed Sevcikova Sehyr

Laboratory for Language and Cognitive Neuroscience, San Diego State University

One of the central assumptions about lexical organization is that the relationship between form and meaning is largely arbitrary. However, this assumption underrepresents characteristic traits of sign languages which contain varying degrees of iconic form-meaning mappings. What role does iconicity play in shaping the lexicon? The study builds on the large-scale data collection methods and visualization techniques that we developed for the ASL-LEX database (http://asl-lex.org/) in which phonological neighborhoods are displayed. The goal of this new study is to visualize and characterize the semantic structure of the ASL lexicon in order to identify areas of iconic and non-iconic systematicity. To accomplish this, we will use information derived from semantic association data from a group of deaf ASL signers, and techniques from network science. In this paradigm, deaf signers are given a cue sign (e.g., CAT) and are asked to produce three signs that immediately come to mind (e.g., DOG, PET, PLAY). A group of trained signers will tag these responses with glosses from the ASL-LEX database. From the data, we will then construct a semantic network graph where nodes represent signs, and edges connecting the nodes are weighted based on the strength of association. To test the feasibility of this paradigm, we conducted a small-scale pilot study with 80 ASL cue signs balanced for frequency and iconicity. Three ASL signers generated three semantic associates for each cue sign in ASL, resulting in a corpus 520 tagged signs. The preliminary results revealed that 335 signs (64%) were connected to at least one other sign in the lexicon with 411 edges, suggesting good overall interconnectivity among signs. Sign pairs like TEACH-STUDENT and TEACH-SCHOOL were strong semantic associates, while TEACH-LEARN were a weakly related pair in the lexicon. Some semantic neighborhoods overlapped with phonological neighborhoods, e.g., iconic signs BELIEVE, HOPE, THINK and KNOW were semantic neighbors which overlap by location (the head) and handshape. The completed study will be the largest dataset of its kind (with ~100,000 lexically-tagged ASL videos) and a valuable resource for further research or sign recognition technologies.