Deep text matching method and device based on word migration learning

A deep and text-based technology, applied in the field of deep text matching methods and devices based on word migration learning, can solve problems affecting model matching effects and achieve the effect of promoting matching accuracy

Active Publication Date: 2019-07-09
ULTRAPOWER SOFTWARE
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Problems solved by technology

[0006] This application provides a deep text matching method and device based on word migration learning to solve the problem that the existing deep matching model parameters are random initialization parameters, which affect the matching effect of the trained model

Method used

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  • Deep text matching method and device based on word migration learning
  • Deep text matching method and device based on word migration learning
  • Deep text matching method and device based on word migration learning

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Embodiment Construction

[0063] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with aspects of the invention as recited in the appended claims.

[0064] Aiming at the problem that the existing model parameters are random initialization parameters, which affect the matching effect of the model, this embodiment provides a basic flowchart of a deep text matching method based on word migration learning, wherein the method can be applied to various Deep matching model.

[0065] figure 1 It is a schematic flowchart of a deep text matching met...

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Abstract

The invention provides a deep text matching method and device based on word migration learning, and the method comprises the steps: firstly, carrying out the fusion of a BERT model and carrying out the pre-training of the BERT model during the training of a deep matching model; secondly, utilizing a pre-trained BERT model to respectively represent sentences in the input sentence pairs by using initial word vectors, and then performing similarity weighting on the sentences in the sentence pairs represented by the initial word vectors to obtain weighted sentence vectors; and finally, according to the loss value corresponding to the similarity value of the statement vector, adjusting model parameters, and carrying out text matching on the input statement by utilizing a depth matching model obtained through parameter adjustment. The parameters of the pre-trained BERT model are no longer randomly initialized parameters, and part-of-speech prediction is added into the pre-trained BERT model,so that the word vector semantic information is enriched. Therefore, semantics, represented by word vectors, of sentences in the sentence pairs are more accurate through the trained BERT model, and the matching accuracy of the trained model is promoted to be improved.

Description

technical field [0001] The present application relates to the technical field of natural language processing, in particular to a deep text matching method and device based on word migration learning. Background technique [0002] Text matching is an important basic problem in natural language processing, and many tasks in natural language processing can be abstracted as text matching tasks. For example, webpage search can be abstracted as a correlation matching problem between webpage and user search query, automatic question answering can be abstracted as a matching problem between candidate answers and questions, and text deduplication can be abstracted as a text-to-text similarity matching problem. [0003] Traditional text matching techniques (such as the vector space model algorithm in information retrieval) mainly solve the matching problem at the vocabulary level. In fact, the matching algorithm based on lexical coincidence has great limitations and cannot solve many...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F17/27
CPCG06F40/289G06F40/30
Inventor 李健铨刘小康晋耀红
Owner ULTRAPOWER SOFTWARE
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