A Collaborative Framework for Discovering the Organizational Structure of Social Networks Using NER Based on NLP

Abstract

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performanceof our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction. ☞ keyword : Semantic Web (씨멘틱 웹), Social Network Analysis (소셜 네트웤 분석), Natural Language Processing (자연어 처리), Machine Learning (기계 학습)