Open AccessGenetic algorithm based sentence packaging in natural language text generation
Author(s) -
Dmitry Devyatkin,
Vadim Isakov,
Alexander Shvets
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/537/4/042003
Subject(s) - computer science , sentence , fitness function , modularity (biology) , artificial intelligence , task (project management) , domain (mathematical analysis) , graph , genetic algorithm , natural language processing , function (biology) , natural language , algorithm , machine learning , theoretical computer science , mathematics , mathematical analysis , management , evolutionary biology , biology , economics , genetics
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.