With the historical text data, images data or speech data, we can build an application that will help to understand the historical terms more effectively and will also broad line the visuals if needed. Using Natural Language Processing techniques like named entity recognition, part-of-speech tagging we can aim for text summarization with the clear perspective of explaining the historical terms. The report can be generated which could be further utilized for analysis for specific incident or event. During learning history, I felt hard to pronounce the names of kingdom and rulers. Thus, we can apply, listen and speak button for difficult words in the document. This will help the user to understand the terms easily. We can also provide visuals for a specific event or specific dates. There are many brainstorming ideas that can be developed using natural language processing and Deep Learning techniques.
In the above user journey figure, if we provide the historical document for example for Berlin and Hamburg cities, it will show the related information about the cities. We can expand our knowledge related to NER and Knowledge graph and achieve wonderful results. In the below paragraph, I am trying to explain NLP concepts with some real-time historical example.
NER is an NLP task that assigns a unique identity to entities mentioned in the text. This can be helpful in text analysis. For example, 100-year-old historical data showing information regarding the German company may want to identify all companies mentioned within a news article, and subsequently, investigate how the relations between the companies might affect the markets.
It is helpful to view NLP as a component within an information extraction pipeline given a text document. As in Fig 2, if we were given a text document consisting of the sentence, “Chancellor Angela Merkel a meet with Prime Minister Narendra Modi”,