How evolutionary algorithms are applied to statistical natural language processing

Statistical natural language processing (NLP) and evolutionary algorithms (EAs) are two very active areas of research which have been combined many times. In general, statistical models applied to deal with NLP tasks require designing specific algorithms to be trained and applied to process new texts. The development of such algorithms may be hard. This makes EAs attractive since they offer a general design, yet providing a high performance in particular conditions of application. In this article, we present a survey of many works which apply EAs to different NLP problems, including syntactic and semantic analysis, grammar induction, summaries and text generation, document clustering and machine translation. This review finishes extracting conclusions about which are the best suited problems or particular aspects within those problems to be solved with an evolutionary algorithm.