Extracting named entities
In many NLP applications, including semantic parsing, we start looking for meaning in a text by examining the entity types and placing an entity extraction component into our NLP pipelines. Named entities play a key role in understanding the meaning of user text.
We'll also start a semantic parsing pipeline by extracting the named entities from our corpus. To understand what sort of entities we want to extract, first, we'll get to know the ATIS dataset.
Getting to know the ATIS dataset
Throughout this chapter, we'll work with the ATIS corpus. ATIS is a well-known dataset; it's one of the standard benchmark datasets for intent classification. The dataset consists of customer utterances who want to book a flight, get information about the flights, including flight costs, flight destinations, and timetables.
No matter what the NLP task is, you should always go over your corpus with a naked eye. We want to get to know our corpus...
