With historical textual information, image data, or speech data, we can build an application that helps us understand historical terms more effectively and also align the visual image when needed. Using natural language processing techniques such as named entity recognition, speech-to-speech notation, we can aim to summarize the text with a clear perspective to explain historical terms. A report can be created that can be further utilized to analyze a particular event or event. During my history study, I found it difficult to pronounce the names of the kingdom and rulers. Therefore, we can retrieve, listen, and speak button for difficult words in the document. This helps the user to understand the terms easily. We can also provide graphics for a specific event or specific dates. There are many ideas that can be developed through natural language processing and in-depth learning techniques.
If we give a historical document to the cities of Berlin and Hamburg, for example, in the travel pattern above, it will show the related information about the cities. We can expand our knowledge of NER and the Knowledge Graph and achieve great results. I will try to explain in the paragraph below NLP concepts and some real-time historical examples.
NER is NLP task who assigns a unique identity to the units mentioned in the text. This can be helpful in text analysis. For example, a hundred-year-old historical data showing information about a German company may want to identify all the companies mentioned in the news article and later examine how the relationships between the companies can affect the market.
It is useful to look at NLP as part of a component in a data acquisition pipeline that has been given a text document. As in Figure 2, if we were given a text document consisting of a sentenceChancellor Angela Merkel will meet with Prime Minister Narendra Mod“,