nlp_named_entity_nltk

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Filename: nlp_named_entity_nltk.py
# Author: Jeoi Reqi

"""
This script performs Named Entity Recognition (NER) using NLTK.

Requirements:
- Python 3
- NLTK library
- 'averaged_perceptron_tagger' and 'maxent_ne_chunker' NLTK resources

Usage:
- Run the script and provide input text when prompted.
- The script will tokenize the text, perform part-of-speech tagging, and identify named entities using NLTK.

Example:
python named_entity_nltk.py
Enter the text: Natural Language Processing is a fascinating field.
Part-of-Speech Tags: [('Natural', 'JJ'), ('Language', 'NNP'), ('Processing', 'NNP'), ('is', 'VBZ'), ('a', 'DT'), ('fascinating', 'JJ'), ('field', 'NN'), ('.', '.')]
Named Entities: (GPE Natural/NNP Language/NNP Processing/NNP)
"""

import nltk
from nltk import pos_tag, ne_chunk
from nltk.tokenize import word_tokenize

# Download the 'averaged_perceptron_tagger' and 'maxent_ne_chunker' resources
nltk.download('averaged_perceptron_tagger')
nltk.download('maxent_ne_chunker')

# Sample text
text = "Natural Language Processing is a fascinating field. It involves the use of computers to understand and process human language."

# Tokenize the text
words = word_tokenize(text)

# Part-of-speech tagging
pos_tags = pos_tag(words)
print("Part-of-Speech Tags:", pos_tags)

# Named Entity Recognition using NLTK
ner_result = ne_chunk(pos_tags)
print("Named Entities:", ner_result)