Trade name recognition method based on full-text attention mechanism

Abstract

The invention discloses a commodity name recognition method based on a full-text attention mechanism. The method comprises the following steps: establishing a depth neural network model with a full-text attention mechanism; adding a full-text attention layer into the depth neural network model; in the full-text attention layer, the full-text information being represented by a method of embedding aword into a vector; the full-text attention layer being represented by a full-text information embedding vector. Then, for each word to be annotated, the similarity between them and the full-text attention-level word vector is calculated, so that the word can obtain the 'attention weight' of the full-text, that is, the word can focus on the context information of the document, and the additionalfeatures of each word can be calculated by using the sum of the attention-level weight, so as to identify the trade name. The method of the invention can effectively extract commodity names from irregular texts, solves the problem of inconsistent identification of the same commodity in context, and improves the accuracy of identification.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a product name recognition method based on a full-text attention mechanism. Background technique [0002] Named entity recognition is a subtask of information extraction that aims to identify proper nouns in text and classify them. Traditional named entity tasks include: person names, place names, organization names, etc. This is a very important basic task in the field of natural language processing, such as: search engines, question answering systems, recommendation systems, translation systems, etc. In recent years, with the continuous development of e-commerce, the task of named entity recognition in the field of e-commerce has also begun to attract people's attention. People urgently need to extract product names from massive and irregular texts and use them In intelligent customer service, advertising recommendation and other fields. [0003] For this...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, provide a product name recognition method based on the full-text attention mechanism, based on deep learning technology, can effectively extract the product name from irregular text, and solve the problem of The problem of inconsistent recognition of the same product in the context improves the accuracy of recognition

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