Symbiosis of evolutionary techniques and statistical natural language processing

Presents some applications of evolutionary programming to different tasks of natural language processing (NLP). First of all, the work defines a general scheme of application of evolutionary techniques to NLP, which gives the mainstream for the design of the elements of the algorithm. This scheme largely relies on the success of probabilistic approaches to NLP. Secondly, the scheme has been illustrated with two fundamental applications in NLP: tagging, i.e., the assignment of lexical categories to words and parsing, i.e., the determination of the syntactic structure of sentences. In both cases, the elements of the evolutionary algorithm are described in detail, as well as the results of different experiments carried out to show the viability of this evolutionary approach to deal with tasks as complex as those of NLP.