Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

Abstract

Most of the traditional methods of English text chunk recognition are solved by setting the corresponding phrase identifier numbers and eventually transforming the chunk recognition problem into a lexical annotation problem. In language recognition, the traditional MFCC features are easily contaminated by noise and have weak noise immunity due to the insufficient amount of information on each frame of the signal. At the same time, SDC feature extraction methods commonly used today require artificial settings in parameter selection, which increases the uncertainty of recognition results. The method of identifying English text chunks by association evaluation of central word extensions identifies English text chunks from a different perspective. It has the following features: (i) each phase is considered as a cluster with the central word as the core, and the internal composition pattern of each phrase is fully considered; (ii) the results are dynamically evaluated using association and confidence. The results show that the proposed method can achieve higher recognition rate than traditional feature extraction methods. The recognition rate is faster, and the F -measure value of English block recognition reaches 94.05%, which is comparable to the best results so far.

Retracted: Analysis of Distributed English Chunk Recognition under Correlation Evaluation Based on Deep Belief Network Model

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”