원문정보
초록
영어
In order to further enhance the performance of syntax parsing, for the shortcomings of hidden Markov model (HMM) in the parameter optimization, an improved syntax parsing method based on adaptive genetic annealing and HMM was presented. First, an adaptive hybrid genetic annealing algorithm was adopted to optimize HMM initial parameters. Second, the improved HMM was trained by Baum Welch algorithm, and then a modified Viterbi algorithm was used to recognize various types of phrases at the same layer, finally a hierarchical analysis algorithm and Viterbi algorithm were combined together to solve hierarchy and recursion in the sentence. In the adaptive genetic annealing HMM algorithm, genetic operators and parameters of simulated annealing (SA) were first respectively improved, subpopulations were classified according to the adaptive crossover and mutation probability of GA in order to realize the multi-group parallel search and information exchange, which could avoid premature and accelerate convergence, then SA was taken as a GA operator to strengthen the local search capability. Compared with several new approaches, Fβ = 1 value is averagely increased by 3%. The experiment results prove that this method is very effective for syntactic parsing.
목차
1. Introduction
2. HMM Description for Syntactic Parsing
2.1. HMM General Description for Syntactic Parsing
2.2. HMM Topology Construction of Phrase Recognition
2.3. Description of Hierarchical Division Process
3. HMM Optimization Training Based on Adaptive Genetic Annealing
3.1. Improvement of Simulated Algorithm
3.2. GA's Parallel Self-adaptation
3.3. HMM Optimization Training Based on Adaptive Genetic Annealing
4. Syntax Parsing Process
4.1. Phrase Identification Process
4.2. Hierarchical Parsing Process
5. Experimental Results and Analysis
6. Conclusions
Acknowledgements
References