This paper studies the computational complexity of disambiguation under probabilistic tree-grammars as in (Bod, 1992; Schabes and Waters, 1993). It presents a proof that the follo...
Recent models of natural language processing employ statistical reasoning for dealing with the ambiguity of formal grammars. In this approach, statistics, concerning the various li...
This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a...
Most probabilistic classi ers used for word-sense disambiguationhave either been based on onlyone contextual feature or have used a model that is simply assumed to characterize th...
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...