Hi! In this post, I will be talking about NER, a very popular and important task in NLP!
Background Named Entity Recognition, which is a subtask of natural language processing (NLP), involves identifying and classifying named entities within a given text. The goal is to identify and categorize the entities present in text. This also forms a step in our goal of relation extraction.
Entities present in text can be calssified(broadly) into a variaty of types like PERSON, ORGANIZATION, LOCATION, TIME, etc. It is easy for humans to identify and categorize entities from text. When we read or hear a sentence like "Messi is a football player of Argentina", we can easily identify that Messi is a person and Argentina is a location/country. Identifying these further helps us understand the relation between them, in this case, the relation is - player of. We can think of named entities as answers to wh questions like Who, What, When ,Where etc. in a sentence. But it is not as easy for a machine to understand and process text as we do. For this, we need either rule based methods or machine learning/deep learning approaches.
...