NER involves detecting entities and then classifying them. Common categories include names, locations, and things. This is an important task that many applications use to support searching, resolving references, and finding meanings in text. The process is frequently used in downstream tasks.

We investigated several techniques for performing NER. Regular expressions are one approach that is supported by both core Java classes and NLP APIs. This technique is useful for many applications, and there are a large number of regular expression libraries available.

Dictionary-based approaches are also possible and work well for some applications. However, they require considerable effort to populate at times. We used LingPipe's MapDictionary class to illustrate this approach.

Trained models can also be used to perform NER. We examined several of these and demonstrated how to train a model using the OpenNLP NameFinderME class. This process was very similar to the earlier training processes.

In the next chapter, Chapter 5, Detecting Parts of Speech we will learn how to detect parts of speech such as nouns, adjectives, and prepositions.