Natural Language Processing: Python and NLTK

Information extraction

We learnt about taggers and parsers that we can use to build a basic information extraction engine. Let's jump directly to a very basic IE engine and how a typical IE engine can be developed using NLTK.

Any sort of meaningful information can be drawn only if the given input stream goes to each of the following NLP steps. We already have enough understanding of sentence tokenization, word tokenization, and POS tagging. Let's discuss NER and relation extraction as well.

A typical information extraction pipeline looks very similar to that shown in the following figure:

Note

Some of the other preprocessing steps, such as stop word ...