Relation extraction based on word embedding with Crowdsourcing Process

For data mining studies, due to the complexity of doing feature selection process in tasks by hand, we need to send some of labeling to the workers with crowdsourcing activities. The process of outsourcing data mining tasks to users is often handled by software systems without enough knowledge of the age or geography of the users' residence. We use convolutional neural network, for doing classification in six classes: USAGE, TOPIC, COMPARE, MODEL-FEATURE, RESULT and PART-WHOLE. This article extracts the data from the abstract of 450 scientific articles and it is a total of 835 relations. One hundred of these abstracts have been selected by the crowdsourcing. Classification results in this article have been done with a slight improvement in accuracy. In this study, we computed the classification results on a combination of vocabulary vectors with using of 450 abstract relation data (100 crowd source datasets with 350 standards). The results of the implementation of the classification algorithm give us performance improvement. This paper uses the population power to perform preparing data mining works. The proposed method by adding crowdsource data to the previous data was able to obtain better results rather than the top 5 methods.