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A novel entity joint annotation relation extraction model
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AI
DOI：10.1007/s10489-021-03002-0.png)
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Abstract
En 中文
The social network is an indispensable part of our life. Text is the most common carrier in social networks. Extracting entities and relationships from a text in social media can help to mine people's views and attitudes. However, identifying the entity pairs that overlap between multiple relations in a sentence and the subject and object that overlap in a relation is a tricky question to be solved urgently. We propose a new relation extraction model named GraphJoint, which models the relation extraction task as a mapping from the relation to the entity. Firstly, we apply the pre-trained BERT encoder to encode the words and generate a text graph for each sentence. We use the graph neural network message-passing mechanism to extract the text features in a sentence, which are used to classify the relations in the sentences. Secondly, we reuse the extracted features and add the relation features to extract the entities. The self-attention mechanism and dilated gate convolution are used to extract entity features further. Finally, we use the joint annotation method to mark the head, tail, and overlapping parts of the subject and the object and transform the task into a sequence labeling task. Experiments compared with other advanced algorithms on two public data sets prove that our method increases the F1 value of the two data sets by 3.6% and 3.4% and achieves a perfect recognition effect in the recognition of overlapping entity pairs.
Keywords:
Relation extraction
Graph neural network
Relation classification
Joint annotation
Natural language processing
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