Unsupervised Morphological Disambiguation using Statistical Language Models

In this paper, we present a probabilistic model for the unsupervised morphological disambiguation problem. Our model assigns morphological parses T to the contexts C instead of assigning them to the words W . The target word w ∈ W determines the possible parse set Tw ⊂ T that can be used in w’s context cw ∈ C. To assign the correct morphological parse t ∈ Tw to w, our model finds the parse t ∈ Tw that maximizes P (t|cw). P (t|cw)’s are estimated using a statistical language model and the vocabulary of the corpus. The system performs significantly better than an unsupervised baseline and its performance is close to a supervised baseline.