Fine-grained entity classification method based on diversified semantic attention model

Abstract

The invention provides a fine-grained entity classification method based on a diversified semantic attention model, and the method comprises the steps of obtaining a diversified attention segment sequence of a sentence based on a segmentation length and a step length; constructing a diversified semantic attention model, wherein the diversified semantic attention model comprises an attention graphprediction model and an attention feature integration model; constructing diversity constraints including attention graph constraints and attention fragment constraints, and determining a final loss function in combination with classification loss for training a diversified semantic attention model; and determining an attention graph corresponding to the diversified attention fragment sequence byusing the trained diversified semantic attention model, predicting a classification result of each time step for each fine-grained entity category in combination with a softmax network, and comprehensively obtaining a prediction result of the entities in the input sentence. According to the invention, the problem of low classification precision of fine-grained entities is solved.

Description

technical field [0001] The invention relates to the field of natural language processing, and relates to a fine-grained entity classification method based on a diversified semantic attention model. Background technique [0002] With the development of natural language processing (NLP), methods for fine-grained entity classification have gradually attracted more and more attention in natural language processing (NLP) applications. Fine-grained entity classification refers to assigning specific types to entities in sentences. Due to the ambiguity of entities, how to utilize fine-grained entity classification methods is a very challenging problem. Not only that, in order to better capture the key differences, the attention mechanism has been applied to automatically select the discriminative features of sentences, which greatly improves the performance of the algorithm. In the prior art, most existing attention methods usually extract salient features from entities and contex...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to propose a fine-grained entity classification method based on a diversified semantic attention model to solve the problem of low accuracy of fine-grained entity classification

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