Ph.D. Candidate: Utku Can Kunter
Program: Cognitive Science
Date: 25.01.2023 / 12:00
Place: A-108

Abstract: Building on an extensive review of the psycholinguistics literature and Turkish Derivational Morphology (DM), we propose a novel structure for representing DM in three hierarchical layers: segmentation, lexical selection and derivation. This proposal involves laying a conventionalized structure over the traditional morphological structure of DM. We develop a computational model of morphology processing based on this structure using Bayesian Belief Networks (BBN). We present an algorithmic implementation for this model that learns and accurately represents new lexical items, recognizes affixes and tracks the salience of each item probabilistically. We carry out trials on this model with realistic observation lists and observe that model predictions are in line with the findings in studies in psycholinguistics. To support our claims and methodology, we carry out an extensive study of Turkish DM, looking into both Modern Turkish and Orkhon Turkic. We also look into the distributional semantics of derivational affixes and observe a high degree of regularity. In order to represent the complex semantics arising from interactions between morphemes, we use the categorial grammar framework. We build a baseline grammar, based on which we construct observation lists for exploration trials. While we focus on Turkish DM, we do not make any language-specific assumptions, our methods and results should be generalizable to other languages with segmental morphology.