Entity relationship extraction method of concerned associated words

Abstract

The invention discloses an entity relationship extraction method of a concerned associated word, and the method comprises the following steps: S1, inputting a labeled text and a to-be-tested text, carrying out text word segmentation, and obtaining a corresponding real value vector of each word mapping; s2, combining the real value vectors corresponding to all the words in pairs to obtain binary combination feature vectors, calculating weight vectors of the binary combination feature vectors and the relation labels, and obtaining binary word group features of the sentences; s3, inputting the real value vector into a neural network layer, and obtaining a semantic structure and a feature vector representation of the text; and S4, connecting the binary phrase characteristics output in the step2 and the semantic characteristics output in the step 3 in series to serve as the representation of a final text sentence, inputting the representation of the final text sentence into a concern layerof a sentence level, obtaining the weights of the sentence for different relation types, and obtaining a final relation classification result and outputting the final relation classification result.

Description

technical field [0001] The invention relates to the fields of deep learning and natural language processing, and in particular to a method for extracting entity relationships focusing on associated words. Background technique [0002] Entity relationship extraction is a hot issue in the field of information extraction. Its main task is to extract entity relationship from unstructured text on the basis of entity recognition, and realize the structured storage and utilization of entity relationship. This technology breaks through the limitations of artificial reading comprehension text semantics and relationship acquisition. It has a speed advantage when dealing with a large amount of text information, and can be applied to many natural language processing applications. For example, through entity relationship extraction, it can assist in the construction of knowledge graphs or ontology knowledge bases; it can also provide support for automatic question answering systems. Fro...

AI Technical Summary

Problems solved by technology

The language features of Chinese texts are complex, and most of them use associative words to further enhance semantic information, while associative words tend to use two words to jointly represent the semantic association between entities, such as "because" and "so" express causality; but now Some entity extraction methods do not pay attention to the influence of associated words on entity extraction, it is difficult to adapt to Chinese text with complex language characteristics, and the accuracy of entity extraction is not high

[0004] For example, the invention patent application with the application publication number CN106202044A discloses a method for extracting entity relationships based on deep neural networks. This method extracts word features, sentence features, and category features, and uses convolutional neural network analysis to obtain the extraction results, which solves the problem of long and short sentences. The problem is that the performance of entity relationship extraction has been improved, but the connection between words has not been paid attention to. For Chinese relationships with complex language characteristics, there is still the problem of low accuracy of entity extraction.

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