An entity relationship joint extraction method and system based on an attention mechanism

Abstract

The invention relates to an entity relationship joint extraction method and system based on an attention mechanism. The method comprises the following steps of: converting an entity marked in trainingdata and a triple of a relationship into a form that each word corresponds to a predefined type of tag; Mapping each word in the sentences of the training data into a corresponding word vector, inputting the word vectors into a neural network model based on an attention mechanism, and performing training through a back propagation algorithm to obtain a label prediction model; And inputting the sentences needing to be subjected to entity relationship extraction into the trained label prediction model, predicting a label corresponding to each word, and obtaining entity relationship triples existing in the sentences according to the corresponding relationship between the labels and the words in the triples. The system comprises a preprocessing module, a model training module and a result processing module. According to the method, by more effectively utilizing the key information in the sentences, the joint extraction performance of the relational entities is improved, and the method hasgood practicability.

Description

technical field [0001] The present invention relates to deep learning and natural language processing technology, in particular to an attention mechanism-based entity-relationship joint extraction method and system. Background technique [0002] In recent years, with the rapid development of Internet information technology, news, social networking and other websites generate massive amounts of new data every day. These data contain a variety of contents, including a lot of very valuable information, which plays a vital role in people's lives. In order to extract and effectively use these valuable information, the concept of knowledge graph is proposed. In the knowledge graph, special nouns such as names of people and places in massive data are represented as entities, and the connection between any two entities is represented as a relationship. Such massive data is represented as a triplet of entities and relationships (entity 1, relationship, entity 2). Although the exis...

AI Technical Summary

Problems solved by technology

Although this type of method uses a neural network to predict the label sequence, it does not distinguish the importance of the words in the sentence from the currently predicted words, so that it cannot make better use of the information of related words while ignoring the irrelevant words. information

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