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Method and system for deep learning problem classification combined with multi-level attention mechanism

A deep learning and attention technology, applied in the field of information processing, can solve the problem that the classification method cannot well integrate the feature extraction capabilities of the two models, and achieve the effect of improving the accuracy

Active Publication Date: 2021-08-31
HEFEI UNIV OF TECH
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Problems solved by technology

[0005] Second: The existing problem classification methods cannot well integrate the feature extraction capabilities of the two models

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  • Method and system for deep learning problem classification combined with multi-level attention mechanism
  • Method and system for deep learning problem classification combined with multi-level attention mechanism
  • Method and system for deep learning problem classification combined with multi-level attention mechanism

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Embodiment Construction

[0053] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0054] Question classification is a basic part of question answering system research, and its accuracy directly affects the system's ability to understand natural language. On the one hand, question classification can provide semantic restrictions and constraints for subsequent information retrieval and answer extraction by determining the target answer type of the question, narrowing the search range of candidate answers, and improving the accuracy of the question answering system. For example, "Where is the most famous Anhui cuisine restaurant?" is a question about the location. When the subsequent answer is extracted, it only needs to match the location type ...

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Abstract

The invention discloses a deep learning problem classification method combined with a multi-level attention mechanism, and relates to the technical field of information processing. The deep learning problem classification method includes: constructing a set of question word vectors, the set of question word vectors includes question word vectors and contains Ordinary word vectors with interrogative word information; according to the set of interrogative word vectors, convolution operation is used to extract the window mapping of the question sentences; according to the window mapping, the temporal features of the questions are extracted; according to the temporal features, the questions are classified. This method strengthens the semantic information of question words in question sentences, and integrates convolutional neural network and long-short-term memory model through the attention mechanism in deep learning, which effectively improves the accuracy of question classification.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a deep learning problem classification method and system combined with a multi-level attention mechanism. Background technique [0002] There are two main types of traditional problem classification methods, one is rule-based methods, and the other is machine learning-based methods. The rule-based method mainly builds a large number of rule bases manually, and adopts the method of rule matching to realize problem classification. The method based on machine learning regards question classification as a supervised learning task, expands the question text by manually formulating features and introducing external corpus, and uses classic support vector machines, naive Bayesian and other machine learning algorithms to classify . [0003] In recent years, due to the maturity of deep learning technology, the problem classification method based on machine learnin...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06F40/289
CPCG06F40/289
Inventor 余本功许庆堂陈杨楠陈能英
Owner HEFEI UNIV OF TECH