A 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. In the present invention, the present invention has a high degree of ambiguity, and the present invention introduces a deep learning language model, which fully considers semantic issues, and can retrieve sentences with high semantic similarity with the target sentence, and use the method of hierarchically calculating semantic similarity Efficiently judge the semantic similarity between sentences; the operation speed is fast, and the vectorized processing is used instead of the conventional loop traversal to process text, ensuring that each semantic matching task unit can be processed in parallel, which greatly improves the search speed; The search recall rate is high, and the use of implication index makes the system more robust to grammatical interference, effectively improving the search recall rate; the system is flexible, and the present invention integrates mechanisms such as semantic understanding, fuzzy query, and precise information positioning , and then encapsulate the entire algorithm module with an interface, which is convenient for users 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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