Biomedicine event trigger word identification method based on characteristic automatic learning

Abstract

The invention relates to the technical field of biomedicine, and relates to a biomedicine event trigger word identification method based on characteristic automatic learning. The biomedicine event trigger word identification method comprises the following steps of 1, data pre-processing; 2, construction of an event trigger word dictionary; 3, construction of candidate trigger word examples; 4, characteristic learning by means of a convolutional neural network model; 5, training by means of a neural network model; and 6, classification of event trigger words. The biomedicine event trigger word identification method is advantaged in that 1, complex preprocessing to data is simplified, and tedious steps for carrying out a characteristic design by people are saved; 2, domain knowledge is introduced, and a lot of external resources such as unlabeled linguistic data are effectively utilized; 3, characteristic automatic learning is carried out by means of a convolutional neural network, manual intervention is reduced, sentence level characteristics in a deeper level can be excavated and explored, through the fusion of local characteristics, implicit global characteristics are discovered, and the category of trigger words can be identified; and 4, a better experiment result is obtained in MLEE linguistic data, and the whole performance on event trigger word detection is improved.

Description

technical field [0001] The invention relates to a biomedical event trigger word recognition method based on feature automatic learning, and belongs to the technical field of biomedical text mining. Background technique [0002] In the field of biomedicine, how to extract useful information from databases containing a large amount of text has important guiding significance for the development of human medicine and life sciences. In view of this, more and more scientific researchers have devoted themselves to the knowledge mining research of biomedical texts, such as named entity recognition in the biological field, relationship extraction between proteins, and relationship extraction between drugs and other research directions. . However, this is still not enough for mining the hidden multivariate relationships in biomedical texts. Therefore, biomedical event extraction tasks that focus on the dynamic interaction or relationship extraction between biological entities such as...

AI Technical Summary

Problems solved by technology

Everything has two sides, and this method also has its limitations. It depends too much on the specific field and text format. The text marking process is time-consuming and laborious and prone to ambiguity. And once the corpus is transferred or changed, the portability of the system will be reduced. It is greatly reduced, and often needs to do a lot of work again, so the actual situation should be considered when selecting

Images