Computational Complexity of Probabilistic Disambiguation

Recent models of natural language processing employ statistical reasoning for dealing with the ambiguity of formal grammars. In this approach, statistics, concerning the various linguistic phenomena of interest, are gathered from actual linguistic data and used to estimate the probabilities of the various entities that are generated by a given grammar, e.g., derivations, parse-trees and sentences. The extension of grammars with probabilities makes it possible to state ambiguity resolution as a constrained optimization formula, which aims at maximizing the probability of some entity that the grammar generates given the input (e.g., maximum probability parse-tree given some input sentence). The implementation of these optimization formulae in efficient algorithms, however, does not always proceed smoothly. In this paper, we address the computational complexity of ambiguity resolution under various kinds of probabilistic models. We provide proofs that some, frequently occurring problems o...

• Type: Journal
• Year: 2002
• Where: GRAMMARS
• Authors: Khalil Sima'an
• Keywords: Ambiguity Resolution, Formal Grammars, Grammars, GRAMMARS 2002