Cynthia Siew Awarded SSHR Fellowship for ‘Measuring the Singaporean Mental Lexicon: Lexical-Semantic Norms for Singapore English Words’
February 23, 2023
Congratulations to Assistant Professor Cynthia Siew (NUS Psychology) on being awarded the Social Sciences and Humanities Research Fellowship for her project titled Measuring the Singaporean Mental Lexicon: Lexical-Semantic Norms for Singapore English Words!
In Measuring the Singaporean Mental Lexicon: Lexical-semantic norms for Singapore English words, funded for S$747,188 by the Social Science and Humanities Research Council in Singapore, Asst Prof Siew seeks to quantify the semantic representations of ~5,000 standard Singapore English and ~3,000 unique Singapore English (Singlish) words. This five year project involves the production of a web application to present these words to Singaporeans, who will provide semantic and affective ratings as well as word associations to the words. For instance, in response to the word “shiok”, Singaporeans will be asked to list the first associations that come to their mind upon seeing the word “shiok”, and provide information about the meaning and emotional properties of “shiok”. The study will use this data to develop an integrated database of semantic, affective, and word association norms for a large set of Singlish words.
Asst Prof Siew expects that the research will generate new knowledge and insights through understanding how cultural and socio-historical aspects of the language environment can shape the structure of cognitive and linguistic representations in human memory. As all data and linguistic norms developed from this research will be eventually made available on open science repositories, the project will also benefit Singapore, strengthening its Digital Defense efforts by augmenting current approaches to semantic analysis of fake news content and detection of misinformation campaigns or foreign interference in online spaces; helping track and quantify public sentiment and optimize public communication campaigns; and complementing and enriching AI-driven natural language processing models with locally validated and normed linguistic data.