Graph convolution-based relationship extraction method

Abstract

The invention provides a graph convolution-based relationship extraction method, which comprises the following steps of: language analysis preprocessing: performing word segmentation and dependency syntactic analysis on an original sentence in a data set by means of a natural language analysis tool to obtain a word segmentation result of the original sentence, and constructing a dependency syntactic tree for representing a semantic dependency relationship between words in the original sentence, generating an adjacent matrix according to a topological relation between nodes in the dependency syntax tree; querying word vectors: converting each word of the original sentence into a corresponding word vector by querying a word vector table to obtain a vectorized representation of the original sentence; feature extraction through the graph convolutional neural network: inputting the adjacent matrix and the vectorized representation of each word into the graph convolutional neural network, and performing learning to obtain feature representation; and relation classification: splicing the feature representations and then sending the spliced feature representations into a learning neural network to obtain a final representation, then obtaining probability distribution of the entity pairs on each relation according to the feature representations, and predicting the relation with the maximum probability, namely the relation type of the subject entity and the object entity in the sentence predicted by the model.

Description

technical field [0001] The invention relates to the field of text data relation extraction, in particular to a relation extraction method based on graph convolution. Background technique [0002] In the era of information explosion, a large amount of text data emerges on the Internet every day, such as news reports, blogs, research documents, and social media comments, etc. How to quickly and effectively mine valuable information from these massive text data has become a Challenges that need to be addressed. Relation extraction is to identify the semantic relationship between named entities for a given text sentence and the marked named entities. [0003] Existing relationship extraction techniques generally feature sentences and words near entities as the input features of the model. After a series of processing, an overall representation is obtained. Finally, the relationship classification probability is obtained after a trained classifier. [0004] Disadvantages of exi...

AI Technical Summary

Problems solved by technology

Statistical learning methods based on feature engineering and kernel functions have considerable shortcomings in model scalability. At the same time, the extraction of these artificially designed features relies on natural language processing tools, and the process of feature extraction is also a pipeline process. , the result of the previous step of natural language processing is used as the input of the next step, so these natural language processing tools are prone to error accumulation and transmission, making the extracted features inaccurate

At the same time, when facing small languages, the lack of relevant natural language processing tools makes the above methods subject to greater limitations

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