Multi-task named entity recognition and confrontation training method for medical field

Abstract

The invention discloses a multi-task named entity recognition and confrontation training method for medical field. The method includes the following steps of (1) collecting and processing data sets, so that each row is composed of a word and a label; (2) using a convolutional neural network to encode the information at the word character level, obtaining character vectors, and then stitching withword vectors to form input feature vectors; (3) constructing a sharing layer, and using a bidirection long-short-term memory nerve network to conduct modeling on input feature vectors of each word ina sentence to learn the common features of each task; (4) constructing a task layer, and conducting model on the input feature vectors and the output information in (3) through a bidirection long-short-term network to learn private features of each task; (5) using conditional random fields to decode labels of the outputs of (3) and (4); (6) using the information of the sharing layer to train a confrontation network to reduce the private features mixed into the sharing layer. According to the method, multi-task learning is performed on the data sets of multiple disease domains, confrontation training is introduced to make the features of the sharing layer and task layer more independent, and the task of training multiple named entity recognition simultaneously in a specific domain is accomplished quickly and efficiently.

Description

technical field [0001] The invention relates to natural language processing, in particular to a multi-task named entity recognition confrontation training method oriented to the medical field. Background technique [0002] Natural Language Processing (NLP) is an interdisciplinary subject integrating linguistics and computer science. Named Entity Recognition (NER) is a basic task in natural language processing, which aims to identify proper nouns and meaningful quantitative phrases in natural language texts and classify them. With the rise of information extraction and big data concepts, the task of named entity recognition has attracted increasing attention, and has become an important part of natural language processing such as public opinion analysis, information retrieval, automatic question answering, and machine translation. How to automatically, accurately and quickly identify named entities from massive Internet text information has gradually become a hot topic in ac...

AI Technical Summary

Problems solved by technology

Due to the lack of training data in the biomedical field, neural network models often perform very poorly

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