@conference{
author = "Mišić, Ksenija and Anđelić, Sara and Osmani, Dajana and Manojlović, Milica",
year = "2023",
abstract = "Previous findings revealed that number of senses and sense probabilities expressed as entropy
predicted processing of polysemous words (Filipović Đurđević & Kostić, 2021; Mišić &
Filipović Đurđević, 2022). However, that had previously only been demonstrated for nouns.
Part of speech was not commonly considered in the lexical ambiguity literature, neither as
theoretical standpoint nor as methodological control (Eddington & Tokowicz, 2015). This
study plans to expand the polysemy research to adjectives and verbs, and to compare the
effects. We estimated the number of senses (NoS) and the distribution of sense probabilities
for 308 Serbian nouns, adjectives, and verbs, and then tested the effects of NoS, entropy (H),
and redundancy (T) on processing of polysemous words. Estimation of H and NoS was done
through a sense production task, where participants listed all senses of a word that they could
remember. Then, words were split across a group of coders who classified the listed senses in
two ways. First, senses were classified into categories formed according to Matica Srpska’s
Dictionary of Serbian language (2011) word senses. Then coders went through the remaining
uncategorised senses and added categories not present in the dictionary. Additional two coders
classified senses on a subsample of words partially overlapping with words of each of the main
coders to compare estimations. Correlations between main coders and control coders varied
from .004 to .971 (mean r = .69, SD = .23; all ps < .05). Using dictionary categories revealed
to be a good strategy when senses are classified by multiple coders, since correlations between
coder estimations were lower for measures calculated when additional categories were
introduced (dictionary: mean r = .78, SD = .24; additional categories: mean r = .59, SD = .17).
This suggests that large-scale categorisations should rely on predefined categories or at least
be guided by them. Measures developed through this categorisation (NoS, H, T) were then used
to predict RTs for three word classes. Our goal was to test whether these measures effects differ
in verbs and adjectives, compared to nouns. Preliminary results of linear mixed-effects
modelling revealed no interaction between NoS/H and word class, however, revealed NoS (b
= -.012, S.E. = .003, df = 287.81, t = -3.62, p = .000) effect across all classes and no H or T
effects. We concluded that our NoS estimations did describe polysemous words
representations, whereas sense probabilities were not adequately captured by our
categorization",
publisher = "Institut za psihologiju, Filozofski fakultet, Univerzitet u Beogradu, Laboratorija za eksperimentalnu psihologiju, Filozofski fakultet, Univerzitet u Beogradu",
journal = "Knjiga rezimea, XXIX naučni skup Empirijska istraživanja u psihologiji, Filozofski fakultet, Beograd",
title = "Estimating the Number of Senses and Sense Probability Distribution for Serbian Polysemous Nouns, Adjectives, and Verbs",
pages = "38",
url = "https://hdl.handle.net/21.15107/rcub_reff_5153"
}
