This paper presents novel methods for generative, discriminative, and hybrid sequence classification for segmentation of Turkish utterances into sentences. In the literature, this task is generally solved using statistical models that take advantage of lexical information among others. However, Turkish has a productive morphology that generates an exponential vocabulary size, harming language models such as the established hidden event language model (HELM). We extend this model as a factored hidden event language model (fHELM) in order to take advantage of morphologically informed features in addition to the word sequence. Our results indicate that fHELMs result in a 26% reduction in error rate for Turkish broadcast news. Combining lexical, morphological, and prosodic information using these new models and discriminative classifiers (boosting and conditional random fields) results in significant performance improvements over any of the classifiers alone.