Unsupervised Russian POS Tagging with Appropriate Context

Abstract

Abstract. While adopting the contextualized hidden Markov model (CHMM) framework for unsupervised Russian POS tagging, we investigate the possibility of utilizing the left, right, and unambiguous context in the CHMM framework. We propose a backoff smoothing method that incorporates all three types of context into the transition probability estimation during the expectation-maximization process. The resulting model with this new method achieves overall and disambiguation accuracies comparable to a CHMM using the classic backoff smoothing method for HMM-based POS tagging from [17].