Text hash retrieval method based on deep learning

Abstract

The invention discloses a text hash retrieval method based on deep learning. The method is characterized by comprising the following steps of: extracting a semantic code corresponding to each piece oforiginal vocabulary data in a word embedding matrix by using a bidirectional LSTM model; then, connecting a text convolutional neural network in parallel behind the bidirectional LSTM model, adding an attention mechanism, converting the output value of the second full connection layer into a corresponding hash code by using a sign function; reconstructing a category label by utilizing hash codes;and finally, searching vector data closest to the Hamming distance of the retrieval text hash code in the text library hash code, and completing the hash retrieval process of the retrieval text data.The method has the advantages that the learning ability of the hash model to the short text is high, the added attention mechanism can further improve the expression ability of the features, the classification layer reconstructs the category labels by utilizing hash codes, so that the hash model can utilize the label information more finely while learning binary codes, and the retrieval precisionis high.

Description

technical field [0001] The invention relates to a text hash retrieval method, in particular to a text hash retrieval method based on deep learning. Background technique [0002] As the scale and dimension of data increase, the cost of semantic retrieval increases sharply. As an important way to achieve efficient semantic retrieval, text hashing has received extensive attention; however, most text hashing algorithms directly use machine learning The mechanism maps the explicit features or keyword features in this paper to binary codes. These features cannot effectively guarantee the semantic similarity between texts, resulting in low retrieval efficiency of the codes. Contents of the invention [0003] The technical problem to be solved by the present invention is to provide a deep learning-based text hash retrieval method with high retrieval accuracy and high efficiency. [0004] The technical solution adopted by the present invention to solve the above-mentioned technica...

AI Technical Summary

Problems solved by technology

[0002] As the scale and dimension of data increase, the cost of semantic retrieval increases sharply. As an important way to achieve efficient semantic retrieval, text hashing has received extensive attention; however, most text hashing algorithms directly use machine learning The mechanism maps the explicit features or keyword features in this paper to binary codes, which cannot effectively guarantee the semantic similarity between texts, resulting in low retrieval efficiency of codes