Abstract
In this paper, through the improved decision tree algorithm, the handles in multimedia English assistance are parsed and simulated. In order to better perceive the sense of language in English composition and improve the rationality of intelligent evaluation, an N element based on association analysis is proposed. Sense value quantification calculates its support in the corpus by obtaining N-tuples of the composition. If the degree of support is lower than the threshold, the part where the language sense problem occurs is analyzed, and the type of language sense problem is judged for the students to provide assistance in modifying the composition. In addition, this paper also extracts word features, sentence features, and text structure features in the composition to fit the English handles analytical score. By testing the test set, the experiment shows that, by extracting the language sense features of the candidate’s English composition, it can not only judge whether there is a problem with the language sense of the candidate, but also provide a basis for the overall evaluation of the composition.
Highlights
With the rapid development of information technology, people can use mobile phones, handheld computers, and other handheld mobile devices to obtain, process, and send information at any time or place, so that communication is everywhere, information is everywhere, and we rely on handheld mobile devices
We focus on the random forest algorithm in the classification algorithm and propose improvements based on the analysis of its principles and characteristics [19]. e improvement mainly includes two aspects: on one hand, the paper explores and optimizes the handle simulation mechanism of the decision tree classification algorithm, performs weighted handle simulation based on the classification effect and prediction probability of the decision tree, and uses the weighted handle simulation to improve the traditional random forest classification algorithm
After analyzing the research status, the vacancies in the current price segment pronunciation error detection research field are summarized, and a classification error detection model method based on machine learning algorithms is proposed
Summary
With the rapid development of information technology, people can use mobile phones, handheld computers, and other handheld mobile devices to obtain, process, and send information at any time or place, so that communication is everywhere, information is everywhere, and we rely on handheld mobile devices. Carrying out educational activities and transmitting educational information with wireless networks has provided the possibility for human lifelong learning [1]. In this era of the popularity of handheld mobile devices, especially smart phones and tablets, almost every college student has one. Ey cannot fundamentally recognize how to pronounce correctly, and pronunciation problems cannot be found in time, and if they are not found, there is no feedback to correct them. Ere are only a few software that has feedback for detecting spoken pronunciation, but the flaw is that the feedback function is not enough to solve the root problem of the learner [3]. It can be pointed out that the learner’s pronunciation is not good enough, but the learner cannot understand where his pronunciation is wrong, and how to improve the Complexity pronunciation, so that the learner cannot get the most valuable feedback to correct the information, and often this does not improve the learner’s oral ability [4, 5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
