Joint Entity And Relation Extraction Based On Deep Learning

With the coming of the big data era,massive amounts of text information have been generated on the Internet.Because a large number of unstructured texts are loosely structured and have cumbersome content,they cannot directly extract key content,which brings challenges to data management,analysis and mining.Therefore,how to extract massive texts on a large scale has become a research hotspot.Information extraction came into being.Entity recognition and relation extraction are key subtasks in the field of information extraction,which are the basic work of many NLP tasks such as knowledge graphs and intelligent Question-Answer and have made outstanding contributions to vertical fields such as finance,law,and medical care.The main purpose is to identify the boundaries and types of named entities from the text and determine whether there is a certain type of relationship between entities.In recent years,deep learning has developed rapidly.Its powerful parameter learning and feature extraction capabilities make up for the shortcomings of traditional machine learning algorithms and artificial features,which are more conducive to the construction of entity recognition and relationship extraction models.Traditional pipeline extraction treats entity recognition and relation extraction as two independent subtasks,which has problems such as error propagation and accumulation,information redundancy,and subtask dependence.This paper conducts research on the joint extraction of entity and relation based on deep learning,investigates the status quo of research in this thesis,and proposes two joint extraction models especially for the shortcomings and deficiencies of existing models.The main works are listed as follows:1.A joint extraction model based on multi-layer pointer network and multi-head selection mechanism is proposed.By using the BERT-based shared encoding layer to establish the dependence between entity recognition and relation extraction,the model can perform two subtasks at the same time,reducing the accumulation of error propagation.Aiming at the entity nesting problem in entity recognition and the relation overlap problem in relation extraction,a multi-layer pointer network and a multi-head selection mechanism are used respectively,so that the model can better handle complex entity relations.In order to improve the generalization ability of the model,auxiliary tasks of global adversarial training are also used.The model has achieved good performance on the data set,and the effectiveness of each part is verified through ablation experiments.2.A joint extraction model combining BERT and multi-head selection mechanism is proposed.On the basis of the aforementioned model,the embedding output based on the BERT encoding layer is reconstructed in order to make better use of the semantic expression of the BERT model and the performance of the BERT output vector constructed in different ways under the current task is analyzed through experiments.In order to better construct the relationship prediction matrix,biaffine calculation is used to replace the original linear transformation,which improves the interaction of relation features between entity pairs;the effectiveness of the model is verified through experiments and compared with the existing joint model.Experiments have achieved better results.