Genetic algorithm based sentence packaging in natural language text generation

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Devyatkin, Dmitry^1 ; Isakov, Vadim^1 ; Shvets, Alexander^1

Federal Research Center Computer Science and Control, Russian Academy of Sciences, Moscow, Russia^1

关键词: Community detection;    Community detection algorithms;    Fitness functions;    High potential;    Machine learning models;    Natural language text;    Semantic structures;    Short texts;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/4/042003/pdf
DOI  :  10.1088/1757-899X/537/4/042003

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Sentence packaging is an important task in natural language text generation which could be treated as a particular kind of a community detection problem. We propose an approach based on genetic algorithm and predictive machine learning models to advance it. The approach allows handling large ontological and semantic structures in a form of a graph to produce well-formed sentences. The results of experiments showed that the genetic algorithm optimizing the modularity measure gives comparable results to ones achieved by a traditional community detection algorithm and outperforms it on a collection of relatively short texts. The design of an approach allows for further introducing linguistic characteristics into a fitness function that gives it a high potential to increase the quality of detected packages while taking into account the specificity of the domain.