Entity linking method based on entity context semantic interaction

Abstract

The invention relates to the technical field of data processing, and discloses an entity linking method based on entity context semantic interaction. The context information of the entity to be linkedwith the attribute description information of the knowledge base entity are combined, a Transformer structure is adopted to encode a knowledge base entity text, an LSTM network is adopted to encode and inquire the entity text, and fine-grained word-level attention interaction is adopted to capture local similar information of the text for semantic encoding of the knowledge base entity text and the inquired entity text. According to the invention, on the basis of using the LSTM and Transformer networks to encode two segments of texts respectively, word-level fine-grained semantic feature interaction is increased; the detail semantic features of the text are enriched, the accuracy rates of 89.1% and 88.5% are achieved on a verification set and a test set and exceed 2.1% and 1.7% of a current mainstream entity link coding model CNN and an LSTM network respectively, and the effectiveness of the entity link method is shown.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an entity linking method based on entity context semantic interaction. Background technique [0002] Entity linking is the process of mapping entity references in natural language to correct candidate entities in a knowledge base. Unstructured natural language representations often have a large number of vague and irregular expressions. In the medical field, such diversity and ambiguity are more common. For example, "Lilac Polygonum" can refer to traditional Chinese medicine for treating diseases such as lung heat and cough, and can also refer to plants of the willow leaf family. As a plant, it is also called "small pomegranate tree", "small pomegranate leaf", "small therapeutic medicine" etc. Accurately understanding the specific entity referred to in the text and linking the entity to the existing knowledge base entity can greatly improve the effectiveness of informat...

AI Technical Summary

Problems solved by technology

Existing research usually uses CNN (Convolutional Neural Network) or LSTM (Long Short-Term Memory Network) to encode entity text, and does not perform further local similarity feature extraction on the interaction between query text and knowledge base text, resulting in possible loss of text detail features

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