We evaluate the effectiveness of finite-state tools we developed for automatically annotating word stress in Russian unrestricted text. This task is relevant for computer-assisted language learning and text-to-speech. To our knowledge, this is the first study to empirically evaluate the results of this task. Given an adequate lexicon with specified stress, the primary obstacle for correct stress placement is disambiguating homographic wordforms. The baseline performance of this task is 90.07%, (known words only, no morphosyntactic disambiguation). Using a constraint grammar to disambiguate homographs, we achieve 93.21% accuracy with minimal errors. For applications with a higher threshold for errors, we achieved 96.15% accuracy by incorporating frequency-based guessing and a simple algorithm for guessing the stress position on unknown words. These results highlight the need for morphosyntactic disambiguation in the word stress placement task for Russian, and set a standard for future research on this task.
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