Paper: Russian Stress Prediction using Maximum Entropy Ranking

ACL ID D13-1088
Title Russian Stress Prediction using Maximum Entropy Ranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

We explore a model of stress prediction in Russian using a combination of lo- cal contextual features and linguistically- motivated features associated with the word?s stem and suffix. We frame this as a ranking problem, where the objec- tive 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 al- lowing for efficient prediction. An empir- ical evaluation shows that a model com- bining the local contextual features and the linguistically-motivated non-local fea- tures performs best in identifying both primary and secondary stress.