Year: 2013
Type: conference-paper
Abstract: We explore a model of stress prediction in Russian using a combination of local contextual features and linguisticallymotivated features associated with the word's stem and suffix.We frame this as a ranking problem, where the objective is to rank the pronunciation with the correct stress above those with incorrect stress.We train our models using a simple Maximum Entropy ranking framework allowing for efficient prediction.An empirical evaluation shows that a model combining the local contextual features and the linguistically-motivated non-local features performs best in identifying both primary and secondary stress.
DOI: 10.18653/v1/d13-1088
Authors: Keith Hall, Richard Sproat
Institution: Google (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2251723447