A medical entity relationship extraction method based on feature fusion

Abstract

The invention discloses a medical entity relationship extraction method based on feature fusion, and the method comprises the steps: enabling entities in a knowledge base to be aligned to medical corpora through a remote supervision and rule combination method, and constructing an entity pair sentence set; performing word-level vector coding on the sentences based on a convolutional neural networkmodel to obtain overall feature vector representation of the sentences; extracting features in left and right subtree directions on the shortest dependency path of the sentences by using a recurrentneural network respectively, and performing splicing operation; and fusing the sentence overall features and the dependency syntax features which are extracted from the two parts respectively, and performing final relation extraction on the obtained fusion features. According to the method, on the premise that a dependency syntax structure is utilized; entity type characteristics capable of expressing entity relationship types among entities are introduced; the position features and the overall features of the sentences are integrated with the dependency syntactic features, the semantic relationship between the sentences is better learned, the interference of noise data on medical entity relationship extraction is reduced, and the accuracy of medical entity relationship extraction can be improved to a certain extent.

Description

technical field [0001] The invention belongs to the field of natural language processing, in particular to a medical entity relationship extraction algorithm based on feature fusion. Background technique [0002] With the advent of the era of medical big data, the knowledge mining and utilization of electronic medical records has received more and more attention. The electronic medical record itself is a kind of semi-structured data, and its structured content provides convenience for automatic computer extraction and analysis. At the same time, the scale of unstructured data is much larger than that of structured data, and it contains rich medical knowledge and patients Fully identifying such knowledge in electronic medical records will greatly promote the development of medical care. [0003] Relation extraction is an important subtask of information extraction. Its main purpose is to convert unstructured or semi-structured natural language text into structured data. Rela...

AI Technical Summary

Problems solved by technology

The existing relationship extraction method based on the shortest dependency path directly takes the syntax extracted by the shortest dependency path as input and ignores the overall characteristics of the sentence, which may cause the lack of important information required by the entity relationship

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