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.
DOI: http://dx.doi.org/10.1088/1757-899X/537/4/042003
PDF at IOPscience: https://iopscience.iop.org/article/10.1088/1757-899X/537/4/042003/pdf
Read or download PDF at ResearchGate: https://www.researchgate.net/publication/333854521_Genetic_algorithm_based_sentence_packaging_in_natural_language_text_generation
Semantic Scholar: https://api.semanticscholar.org/CorpusID:53236103
Devyatkin D., Isakov V., Shvets A. Genetic algorithm based sentence packaging in natural language text generation // IOP Conference Series: Materials Science and Engineering. – IOP Publishing, 2019. – Vol. 537. – №. 4. – p. 042003.