Natural language processing with Python

Natural language processing is a field of artificial intelligence that focuses on the interaction between computers and humans using natural language. In this tutorial, we will be using Python to perform basic text processing tasks using the Natural Language Toolkit library, also known as NLTK. Step 1: In this step, we will be tokenizing a given text. Tokenization is the process of breaking down a text into individual words or sentences. We will use the NLTK library to tokenize a sample text. 
import nltk
from nltk.tokenize import word_tokenize

text = "Natural language processing is a fascinating field of study."
tokens = word_tokenize(text)

print(tokens)

Step 2: Next, we will be performing part-of-speech tagging on the tokenized text. Part-of-speech tagging is the process of assigning a part of speech to each word in a text. We will use NLTK's pos_tag function to achieve this. 
from nltk import pos_tag

pos_tags = pos_tag(tokens)
print(pos_tags)

Step 3: In this step, we will be performing named entity recognition (NER) on the text. Named entity recognition is the process of identifying named entities such as names, locations, and organizations in a text. We will use NLTK's ne_chunk function to perform NER. 
from nltk import ne_chunk
nltk.download('maxent_ne_chunker')
nltk.download('words')

ner_tags = ne_chunk(pos_tags)
print(ner_tags)

By following these steps and running the provided Python code, you will be able to perform basic natural language processing tasks using NLTK.

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