Semantic Word Segmenting Technique Based on Artificial Bee Colony Algorithm

Because of the complexity and uncertainty of sentences, ambiguous information often appears in sentence segmentation. To the sentence ambiguity division problem, we proposed a method that can solve the semantic information in the sentence and can select the breakpoint of word dynamically. This study proposed and designed a single-objective discrete optimization model based on the ontology technology of lexeme semantic information. This model analyzed the semantic information of the sentences through the ontology data analysis, combined the case data to calculate the relevant semantic word frequency information; then proposed an optimized artificial bee colony algorithm to solve the model, by introduced an adaptive competition strategy in the selection and elimination of the population to accelerate the convergence speed of the algorithm. Finally, this study experimented on the word segmentation module of the library automatic question-answer consultation system. The experimental results showed that this method can meet the user’s requirements, improve the question segmentation accuracy, accelerate the convergence speed of the bee colony, which verified the effectiveness of the proposed method.