Event extraction method, related device, equipment and storage medium

Abstract

The embodiment of the invention discloses an event extraction method, a related device, equipment and a storage medium, which are used for converting a sentence-level natural language into nodes and edges and then converting the nodes and the edges into semantic features to perform event extraction, so that the accuracy of event acquisition can be ensured. The method of the embodiment comprises: obtaining a to-be-processed text; generating abstract semantic representation according to the to-be-processed text, wherein the abstract semantic representation comprises nodes in one-to-one correspondence with the words and edges used for connecting the nodes; carrying out semantic coding processing on the abstract semantics and the text representation to obtain a semantic embedding vector, wherein the semantic embedding vector is used for representing semantic features between each word and the event; carrying out graph coding processing on the abstract semantic representation to obtain a graph embedding vector, wherein the graph embedding vector is used for representing structural features of nodes connected through edges; splicing the semantic embedding vector and the graph embedding vector to obtain a spliced feature vector; and identifying the spliced feature vector, and outputting a target event.

Description

technical field [0001] The embodiments of the present application relate to the technical field of the Internet, and in particular to an event extraction method, related devices, equipment, and storage media. Background technique [0002] Event extraction is to extract events of interest to users from unstructured information and present them to users in a structured manner. Generally, event detection is performed on the information first, and then event role extraction is performed on the detected events. [0003] The traditional event extraction method mainly adopts the method of supervised learning. Generally, it is necessary to manually label the data set labels to obtain standard corpus, and define the structure of the events in the data set in advance, and then use the marked corpus and Define a well-defined event architecture to train the advanced neural network, and then perform event extraction through the trained advanced neural network. [0004] Although the trad...

AI Technical Summary

Problems solved by technology

[0004] Although the traditional event extraction method does not depend on the content and format of the corpus, the construction of the event extraction data set is difficult and requires a large-scale standard corpus, otherwise there will be more serious data sparse problems, resulting in limited training of advanced neural networks , resulting in poor performance of event extraction

Images