Joint Extraction Method of Biomedical Events Based on Replication Mechanism

Abstract

The invention provides a biomedical event extraction method based on a replication mechanism, which belongs to the technical field of natural language processing. The steps of the biomedical event extraction method based on the replication mechanism are as follows: construct the model input vector; construct the Encoder module using the bidirectional LSTM model; construct the Decoder module that simultaneously recognizes trigger words and elements based on the Attention mechanism and the replication mechanism. The present invention can effectively avoid cascading errors caused by the staged method and the shortcomings of mutual independence between subtasks, as well as the error transfer caused by simply using shared parameters in the joint model, and improve the performance of biomedical event extraction.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and relates to an event joint extraction method based on biomedical text. Specifically, it refers to using the joint extraction method through the replication mechanism to simultaneously extract trigger words and elements in biological events to form biological event candidates. Then classify the biological event candidates through the event structure features learned by the support vector machine (SVM), and remove the invalid combination pairs, so as to obtain a complete biomedical event. Background technique [0002] At present, there are two main types of methods to achieve biological event extraction. One is a staged method (also known as a pipelined method), which divides the extraction of biological events into two main steps: trigger word recognition and element recognition, and then constitutes a complete biomedical event through post-event processing. The other is t...

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Problems solved by technology

[0010] The staged method makes the biological event extraction task more hierarchical, but there are also some problems: (1) error propagation

Since the feature recognition stage needs to use the trigger words predicted in the trigger word recognition stage, if the trigger word recognition effect is not good, the error in the trigger word stage will be propagated to the feature recognition stage, resulting in cascading errors

(2) Neglecting the relationship between two subtasks

But this method still has some disadvantages: (1) When using the joint extraction method for biological event extraction, the extracted features depend on NLP preprocessing tools, which may cause errors

This method can reduce the independence between the two subtasks, but there is still the problem of error transmission

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