Abstract
This paper discusses the application of computational linguistics in the machine learning (ML) system for the processing of garden path sentences. ML is closely related to artificial intelligence and linguistic cognition. The rapid and efficient processing of the complex structures is an effective method to test the system. By means of parsing the garden path sentence, we draw a conclusion that the integration of theoretical and statistical methods is helpful for the development of ML system.
Highlights
Machine learning (ML) focuses on the creation of algorithms used to help computers to evolve behaviors on the basis of empirical data
It is related to the computational applications, including data mining programs to find the general rules in large data sets and information filtering systems to automatically learn users' interests
Machine learning (ML) system concerns the design of algorithms helpful for computational development and linguistic cognition
Summary
Machine learning (ML) focuses on the creation of algorithms used to help computers to evolve behaviors on the basis of empirical data. ML is closely related to software and artificial intelligence (AI) It highlights the rapid and effective applications of decision making in the domains of engineering and computational linguistics. The transferring human control skill to an automatic controller, e.g. the behavioral cloning, is becoming another focus of ML [3] Both statistical-based and logic-based techniques are effective. The principal component analysis (PCA) can reduce high-dimensional spectral data and improve the predictive performance of ML skills by means of the classification of high-dimensional data [5]. The dropout prediction method for elearning courses is proved to be effective It is based on a kind of ML technique which is feed-forward neural networks. The effective ML models involved in the language processing will be discussed below
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