Contextualising government reports using Named Entity Recognition

Detta är en Kandidat-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: The science of making a computer understand text and process it, natural language processing, is a topic of great interest among researchers. This study aims to further that research by comparing the BERT algorithm and classic logistic regression when identifying names of public organizations. The results show that BERT outperforms its competitor in the task from the data which consisted of public state inquiries and reports. Furthermore a literature study was conducted as a way of exploring how a system for NER can be implemented into the management of an organization. The study found that there are many ways of doing such an implementation but mainly suggested three main areas that should be focused to ensure success - recognising the right entities, trusting the system and presentation of data.

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