Semantic fuzzy search method based on sentence-level deep learning language model

Abstract

The invention discloses a semantic fuzzy search method based on a sentence-level deep learning language model. According to the method, the fuzzy degree is high, a deep learning language model is introduced, the semantic problem is fully considered, statements with high semantic similarity with target statements can be retrieved, and the semantic similarity between the statements is efficiently judged in a layered semantic similarity calculation mode; the operation speed is high, vectorization processing is used for replacing a conventional text cyclic traversal processing mode, all semantic matching task units are ensured to be processed in parallel, and the search speed is greatly increased; the search recall ratio is high, and implication indexes are utilized, so that the robustness ofthe system to grammar interference is better, and the search recall ratio is effectively improved; the system is flexible, the mechanisms of semantic comprehension, fuzzy query, accurate information positioning and the like are fused, and then the whole algorithm module is subjected to interface encapsulation, so that convenience is brought to a user to call.

Description

technical field [0001] The present invention relates to the relevant field of deep learning language model and fuzzy search, in particular to a semantic fuzzy search method based on sentence-level deep learning language model. Background technique [0002] Language models are widely used. As early as the 1970s, statistical language models have been successfully applied. In 2003, some scholars proposed to apply the concept of word vector to neural network-based language models. However, This kind of model has many parameters, and the training is more complicated; in 2010, some scholars proposed to apply the Recurrent Neural Network (Recurrent Neural Network) to the language model, which opened the wide use of deep learning in the language model, and later A series of excellent language models have emerged, such as: ELMO (Embedding from Language Models) model, Transformer model, and BERT (Bidirectional Encoder Representations from Transformer) model. Text fuzzy search is appl...

AI Technical Summary

Problems solved by technology

[0004] 1. Most of the current text fuzzy search cannot solve the problem of semantic understanding of the text, which makes the search recall rate low, because if the problem of semantic understanding is not considered, many texts with the same meaning but different language structures cannot be matched;

[0005] 2. Most of the current fuzzy text searches are relatively inefficient. When searching for keywords or key sentences in relatively long texts, the efficiency is relatively low due to the use of violent methods to process the text;

[0006] 3. The current text fuzzy search cannot solve the problem of semantic deviation of keywords or key sentences caused by the context in the text when analyzing the semantic understanding of the text, which will reduce the recall rate of the search, and in the When analyzing semantic similarity, a relatively single similarity measurement index is used, which cannot accurately calculate the similarity between semantics, that is, it cannot efficiently measure the correlation between semantics

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