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.
Highlights
Block recognition is the main element of shallow analysis, which can be applied to information retrieval, machine translation, subject content analysis, and text processing, and the accuracy of block recognition is directly related to the correctness of text analysis and text processing
The experimental results show that the BN-deep belief network (DBN) method can improve the recognition accuracy more effectively than the traditional language recognition methods Mel-Frequency Cepstral Coefficient (MFCC) and Shifted Delta Cepstra (SDC) [16]
The central lexical property is selected first, and the boundaries of the phrase are expanded by the correlation between two adjacent lexical properties, and an error-driven method is applied to evaluate and correct the correlation to improve the accuracy of phrase recognition
Summary
Block recognition is the main element of shallow analysis, which can be applied to information retrieval, machine translation, subject content analysis, and text processing, and the accuracy of block recognition is directly related to the correctness of text analysis and text processing. [4] used the machine learning algorithm Winnow for English chunk recognition and obtained the best results reported so far (with an accuracy rate of over 94.28%). The advantage of this algorithm is that it can identify features relevant to itself from a large number of features, but the use of a large number of features makes the query inefficient; the use of lexicalized features leads to sparse data [5]. The experimental results show that the BN-DBN method can improve the recognition accuracy more effectively than the traditional language recognition methods MFCC and SDC [16]
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