Welcome to the NLG tutorial! Natural Language Generation (NLG) is the art of creating human-like text using algorithms. This tutorial will guide you through the basics of NLG and help you get started on your own projects.
What is NLG?
Natural Language Generation (NLG) is the process of generating natural language text from data. This can be used for a variety of applications, such as creating automated reports, generating summaries, or even writing stories.
Key Components of NLG
- Data: The raw information that will be used to generate the text.
- Grammar: The rules that dictate how the text should be structured.
- Lexicon: The vocabulary that will be used in the text.
Getting Started
To get started with NLG, you'll need a few things:
- A programming language: Python is a popular choice for NLG due to its simplicity and the availability of libraries.
- A library: There are several libraries available for NLG, such as NLTK, spaCy, and Transformers.
- Data: You'll need a dataset to train your NLG model.
Example: Summarizing News Articles
One common use case for NLG is summarizing news articles. Here's a simple example of how you might do this using the Hugging Face Transformers library:
from transformers import pipeline
summarizer = pipeline("summarization")
article = "This is a sample news article. It contains important information that needs to be summarized."
summary = summarizer(article, max_length=150, min_length=30, do_sample=False)
print(summary[0]['summary_text'])
Resources
For more information on NLG, check out the following resources:
Natural Language Generation