Dissertations, Theses, and Capstone Projects
Date of Degree
9-2022
Document Type
Thesis
Degree Name
M.A.
Program
Linguistics
Advisor
Kyle Gorman
Subject Categories
Computational Linguistics
Keywords
stress, neural network, prediction, Russian
Abstract
In the Russian language, stress on a word is determined via often complex patterns and rules. In this paper, after examining nearly a century of research in stress rules and methods in Russian, we turn to see if modern machine learning tools can aid in predicting stress. Using A.A. Zaliznyak’s dictionary grammar and over 300,000 word forms, we derived stress codes to aid in predicting which syllable primary stress falls on. We trained an LSTM neural network on the data and conducted eight experiments with added features such as lemma, part of speech, and morphology. While the model performed better than baseline in most experiments, the lemma feature outperformed every other feature.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Schriner, John, "Predicting Stress in Russian using Modern Machine-Learning Tools" (2022). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/4974