A Novel Disambiguation Method For Unification-Based Grammars Using Probabilistic Context-Free Approximations

摘要

We present a novel disambiguation method for unification-based grammars (UBGs). In contrast to other methods, our approach obviates the need for probability models on the UBG side in that it shifts the responsibility to simpler context-free models, indirectly obtained from the UBG. Our approach has three advantages: (ⅰ) training can be effectively done in practice, (ⅱ) parsing and disambiguation of context-free readings requires only cubic time, and (ⅲ) involved probability distributions are mathematically clean. In an experiment for a mid-size UBG, we show that our novel approach is feasible. Using unsupervised training, we achieve 88% accuracy on an exact-match task.