The Design And Implementation Of Crowdsourcing Platform For Text Labeling System Based On Android

Text information is the most basic form of information,and natural language processing technology can be used to analyze and process massive amounts of text data.The first condition for processing information intelligently and automatically is to own the text data that has already been labeled as thetraining set to train the data model.Therefore,labeling text data has become a problem to be solved before the study on natural language processing algorithms.Because there are many kinds of text processing algorithms,it is necessary to study the text at different angles,and it is necessary to implement multiple types of text labeling.This thesis has summarized the development status of data labeling platform at home and abroad,aiming at the characteristics of the current data labeling platform:there are many kinds of data labeling types,but there are few professional text labeling platforms;combined with the characteristics of crowdsourcing platform:users with large quantity,high efficiency and low cost,so the necessity and feasibility of constructing a crowdsourcing-based text labeling system is proposed to solve the data labeling problem effectivelyThis thesis has designed and implemented a text labeling system based on crowdsourcing platform.The system is divided into three modules:task publishing module task executing module and task management module.In this system,the text labeling work is task-based and the text labeling tasks are divided into different types.In the task publishing module,users can select a text labeling type,and then upload the text content that wants to be labeled to the system in the form of file.In the task executing module,users can choose different ways of operation such as selecting file content,selecting labels,connecting lines and draging text to implement different types of text labeling.In the task management module,users can view tasks that are published or participated in by himself.The system's back end uses the Spring Boot framework to build,and the front end uses Android mobile pages to display data.The system has designed and implemented six types of text labeling to label text,and has completed the expected functions,the system can extend new text labeling types in late period.The system is dedicated to providing high-quality,multi-category and reliable labeling data sets for all algorithms of natural language processing;and improving the accuracy of algorithm training by using reliable data,reducing the preparation time required for training algorithms,and promoting the development of natural language processing technology.