Yasemin Gunal
Pronouns: She/her/hers
Research Mentor(s): Lisa Levinson
Co-Presenter:
Research Mentor School/College/Department: Linguistics / LSA
Presentation Date: April 20
Presentation Type: Poster
Session: Session 2 – 11am – 11:50am
Room: League Ballroom
Authors: Yasemin Gunal, Lisa Levinson
Presenter: 79
Abstract
Previous research suggests that there are processing effects, such as word length, frequency, and age-of-acquisition, in real-time human word processing experiments that impact the results of lexical decision making (Sedivy 2020). Specifically, McKoon and Love (2011) have previously explored the various elements of verbs and verb complexities that cause observable impacts on human verb processing. By creating Python scripts designed to measure the lexical properties that influence linguistic processing, this project aims to leverage computational linguistics methodologies to improve the lexical statistics and analyses of a replication study of McKoon and Love’s research. The Python scripts measure the frequency of verb occurrences, the number of meanings a verb may have, and the parts of speech of words in a given context. Based on McKoon and Love’s stimuli, the total number of senses that each word has, and the number of senses that each word has as a verb specifically, were determined using WordNet (Princeton University 2010). Additionally, the Corpus of Contemporary American English was used to obtain the frequencies of each word, which were then converted into probabilities of the words occurring. These computational scripts were then applied to the statistical analysis of the experiments for the replication study to help navigate the complex layers of human word processing and to draw more accurate conclusions about word processing beyond surprisal theory (Hale 2001).
Social Science
