In this chapter, we learned about the task of Named Entity Recognition (NER) and how that works in practice. We reviewed the characteristics of a named entity, and compared many strategies for finding named entities in text and classifying found entities into their correct type. We implemented a simple NER program using NLTK and used it to detect named entities in four different types of technical communication: chat, chat summaries, e-mails, and meeting minutes. We calculated the accuracy of our NER program using precision, recall, and the F1-measure against each of these text samples, and learned how the characteristics of the text sample will affect the accuracy of the program.
One of the outcomes of this chapter was to demonstrate that text that is written in plain language with fewer technical terms will be easier to mine for named entities than very technical language with a lot of code snippets, function names, acronyms, and the like. We noticed that we got the best results...
