Deep learning-based asymmetric text hashing method

Abstract

The invention provides a deep learning-based asymmetric text hashing method. According to the method, the semantic consistency of hash codes is maintained by using a difference value between binary hash code similarity and semantic similarity among minimized texts, so that the binary codes have similar information retention; a heterogeneous neural network is used for carrying out hash learning respectively on retrieving texts and retrieved texts, so that the efficiency of text hash learning is improved.

Description

technical field [0001] The present invention relates to the fields of text information retrieval and deep learning, and more specifically, relates to an asymmetric text hashing method based on deep learning. Background technique [0002] With the development of the Internet, various websites emerge in endlessly. As a general text retrieval tool, search engine has become an important entrance linking users and websites. In traditional search engines, users need to input some keywords, and the search engine uses indexing technology to retrieve articles that are highly relevant to user requests based on these keywords. In recent years, with the advancement of natural language processing technology and the development of social network and multimedia technology, there has been a need for richer text retrieval, such as: question answering system, voice assistant, recommendation system, etc. These requirements make the text retrieval method based on keyword matching start to enc...

AI Technical Summary

Problems solved by technology

[0008] However, the current text hashing learning method only uses a single hash function to model both the retrieval and the retrieved text. However, in real applications, the representation of the retrieved text and the retrieved text is very different.

This leads to the fact that these hashing methods cannot accurately retrieve valid information

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