ChatGPT + Python + NLTK: A BeginnersGuide | by Gabe Araujo, M.Sc. | Nov, 2023
As an AI fanatic and a seasoned Python developer, I’ve at all times been fascinated by the ability of language and its processing. On this period the place textual knowledge is extra considerable than ever, extracting that means and gaining insights from textual content may be fairly a frightening job. That is the place the mix of ChatGPT, Python, and the Pure Language Toolkit (NLTK) comes into the image.
NLTK is a number one platform for constructing Python applications to work with human language knowledge. It gives easy-to-use interfaces to over 50 corpora and lexical sources resembling WordNet, together with a collection of textual content processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
Let’s start by establishing the environment. First, we’ll want Python put in on our system. Python is a superb selection for pure language processing (NLP) resulting from its simplicity and the huge array of libraries accessible.
As soon as Python is put in, you may set up NLTK by way of pip:
pip set up nltk
After putting in NLTK, we will begin by importing it and downloading the mandatory knowledge:
nltk.obtain('standard') command fetches the most well-liked packages and corpora, that are a staple in any NLP job.
Tokenization is the method of splitting textual content into particular person phrases or phrases. NLTK gives tokenizers for varied duties.
from nltk.tokenize import word_tokenize
textual content = "ChatGPT is revolutionizing the best way we work together with AI."
tokens = word_tokenize(textual content)
This snippet will output a listing of tokens:
['ChatGPT', 'is', 'revolutionizing', 'the', 'way', 'we', 'interact', 'with', 'AI', '.']
Tokenization is commonly step one in textual content evaluation.
NLTK may categorize phrases into their elements of speech. This may also help us perceive the construction of a sentence.
from nltk import pos_tag