Interrogative Sentences Recognition Based on the GRU Multiattentive Layer Model

Abstract

Understanding the question is the key point in the question answering system. Therefore, this paper designs a multi attention layer model, extracts some missing features from the storage module, and uses Gru unit to organize them reasonably. By using non-linear operations to combine the results of different attention layers, we can avoid extracting only the linear combination of memory modules. Effectively combine the process of problem understanding, including the classification of problem words, the vocabulary annotation of problem sentences, the identification of problem sentence patterns, the classification of problems and the identification of problem centers, the grammatical analysis of question sentences and the identification of question sentence patterns, so as to achieve problem understanding. Experimental results show that this method improves the accuracy of problem understanding.

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

“…Two-Way Loop GRU. e three types of recurrent neural networks can process time series, but the information flow can only be passed from the front to back in time [27,28]. Recurrent neural networks are so dependent on the order of data that the features extracted by forwarding processing of data and reverse processing of data by using recurrent neural networks may be completely different.…”

Retracted: Interrogative Sentences Recognition Based on the GRU Multiattentive Layer 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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1].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:…”