Interrogative sentence similarity measurement method based on deep convolutional neural network

Abstract

The invention provides an interrogative sentence similarity measurement method based on a deep convolutional neural network. The method comprises the following steps that: S1: through a knowledge domain related page, generating a raw corpus, crawling Chinese characters which appear in raw language materials, and generating a word vector corresponding to each Chinese character; S2: replacing each Chinese character in an interrogative sentence by the corresponding word vector to obtain a word vector set corresponding to the interrogative sentence, wherein the word vector set carries out calculation through the convolutional neural network to obtain a corresponding sentence meaning vector; and S3: carrying out pairwise combination on the interrogative sentences, and calculating the cosine function absolute values of the sentence meaning vectors corresponding to two interrogative sentences to obtain a similarity between two interrogative sentences. By use of the method, an individual-character analysis method is adopted to avoid an influence on subsequent analysis by word segmentation errors, the convolutional neural network takes the whole interrogative sentence as a whole to extractwhole sentence characteristics, and a sentence meaning splitting problem brought by using word similarity matrixes is avoided.

Description

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AI Technical Summary

Benefits of technology

In this new technology, it uses advanced techniques such as analyzers or machine learning algorithms to analyze data from large amounts of unstructured documents without any mistakes made during previous processing steps like tokenization. It achieves these technical results through specific methods called convolutional Neural Networks which can identify patterns within complex datasets with high accuracy.

Problems solved by technology

Technological Problem addressed in this patents relating to traditional search robot systems involves finding matches that match users' queries accurately despite their ambiguous or incorrect phrases being used frequently during conversations (such as technical problem). Current solutions involve complex algorithms like phrase analysis and machine learning techniques such as nearest neighbor searching.

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