The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them.However,the volume of internet data is beyond the processing capabilities of the current internet infrastructure.Therefore,engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia.The goal of this paper is to explore the entity relationship based on deep learning,introduce semantic knowledge by using the prepared language model,develop an advanced entity relationship information extraction method by combining Robustly Optimized BERT Approach(RoBERTa)and multi-task learning,and combine the intelligent characters in the field of linguistic,called Robustly Optimized BERT Approach+Multi-Task Learning(RoBERTa+MTL).To improve the effectiveness of model interaction,multi-task teaching is used to implement the observation information of auxiliary tasks.Experimental results show that our method has achieved an accuracy of 88.95 entity relationship extraction,and a further it has achieved 86.35%of accuracy after being combined with multi-task learning.

A Top-down Method of Extraction Entity Relationship Triples and Obtaining Annotated Data

The extraction of entity relationship triples is very important to build a knowledge graph(KG),meanwhile,various entity relationship extraction algorithms are mostly based on data-driven,especially for the current popular deep learning algorithms.Therefore,obtaining a large number of accurate triples is the key to build a good KG as well as train a good entity relationship extraction algorithm.Because of business requirements,this KG’s application field is determined and the experts’opinions also must be satisfied.Considering these factors we adopt the top-down method which refers to determining the data schema firstly,then filling the specific data according to the schema.The design of data schema is the top-level design of KG,and determining the data schema according to the characteristics of KG is equivalent to determining the scope of data’s collection and the mode of data’s organization.This method is generally suitable for the construction of domain KG.This article proposes a fast and efficient method to extract the topdown type KG’s triples in social media with the help of structured data in the information box on the right side of the related encyclopedia webpage.At the same time,based on the obtained triples,a data labeling method is proposed to obtain sufficiently high-quality training data,using in various Natural Language Processing(NLP)information extraction algorithms’training.

Citizen-Centric Complaint Reporting and Analyzing Mechanism

The “Citizen-Centric Complaint Reporting and Analyzing Mechanism” project is designed to create an online complaint system, called “e-Complaint”, to allow citizens to file complaints related to crime and misconduct in a secure and user-friendly way. The proposed system aims to address the challenges of the current complaint system, ensuring transparency and accountability in the police force. The “e-Complaint” system aims to increase police accountability and transparency and has significant benefits for both citizens and police departments.