Grammatical category disambiguation by statistical optimization 论文

摘要

[This paper focuses on the]... task of [part-of-speech] disambiguation, and particularly on a new algorithm called VOLSUNGA, which avoids syntactic-level analysis, yields about 96% accuracy, and runs in far less time and space than previous attempts. The most recent previous algorithm runs in NP (Non-Polynomial) time, while VOLSUNGA runs in linear time. This is provably optimal; no improvements in the order of its execution time and space are possible. VOLSUNGA is also robust in cases of ungrammaticality. Improvements to this accuracy may be made, perhaps the most potentially significant being to include some higher-level information. With such additions, the accuracy of statistically-based algorithms will approach 100%; and the few remaining cases may be largely those with which humans also find difficulty. In subsequent sections we examine several disambiguation algorithms. Their techniques, accuracies, and efficiencies are analyzed. After presenting the research carried out to date, a discussion of VOLSUNGA's application to the Brown Corpus...