Evolutionary computation for NLP tasks
Ana Paula Silva, Arlindo Silva · Institution of Engineering and Technology eBooks · 2018
Natural language processing (NPL) is an important research field that deals with very different problems (tasks) concerning how to interpret, generate and process natural language. Different approaches have been proposed to tackle these problems. More recently, a significant number of works that use evolutionary computation to solve some of them were presented. Among these, we can find attempts to solve the problem of word segmentation, part-of-speech tagging, syntactic sentence analysis and grammar generation. Despite the good results obtained by these approaches, these techniques are still not widely used by the community of researchers working in the area of NPL. With this chapter, we aim to contribute to the dissemination of these relatively recent global optimisation techniques as valid alternatives to the classic approaches normally used to tackle these problems. To achieve this, we begin by making a description of these algorithms, sufficiently exhaustive, in our opinion, to understand their fundamental aspects. Next, we present, in detail, the most representative works found in the literature that apply evolutionary computation-based techniques to the tasks mentioned above.