Entity relationship extraction method and system integrated with dynamic word vector technology

Abstract

The invention provides an entity relationship extraction method and system integrated with dynamic word vector technology. According to the system, an existing knowledge base corresponds to rich unstructured data by using a remote supervision method so as to generate a large amount of training data, so that the problem of insufficient manual annotation corpora is relieved, and the system can reduce the dependence on annotation data, thereby effectively reducing the labor cost. In order to obtain feature information between entities as much as possible, the basic architecture of the model adopts a segmented convolutional neural network; and the semantic information of the example sentences is further extracted by integrating a dynamic word vector technology.

Description

technical field [0001] The invention relates to the field of information extraction, specifically, it mines the semantic relationship between entities. Background technique [0002] Information extraction aims to extract structured information from large-scale unstructured or semi-structured natural language texts. The main tasks include entity extraction, relationship extraction, and event extraction. Among them, the main content of relation extraction (Relation Extraction, RE) research is to dig out the semantic relationship between entities and entities from the text content, and use the relationship extraction technology to dig out the deep relationship structure between entities, which has profound theoretical significance and Huge research value, it is also the basic work of optimizing search engines, building knowledge graphs, and developing intelligent question answering systems. [0003] Practice has proved that supervised learning methods can extract more effectiv...

AI Technical Summary

Problems solved by technology

[0004] The use of neural network models mainly faces two problems: (1) There is not enough labeled data, the training data set has a low coverage of entities and entity relationships, and it does not perform well in terms of versatility

Moreover, it takes a lot of time and energy to manually label training data; (2) Since word usage is complex and changeable in semantics and grammar, existing models use pre-trained word vectors that are "static" and cannot follow the language changing environment

As a result, its ability to represent is limited

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