Python for Natural Language Generation (NLG)

• Python programming experience

Audience

• Developers
• Data scientists

Natural Language Generation (NLG) refers to the production of natural language text or speech by a computer.

In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.

By the end of this training, participants will be able to:

• Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting.
• Select and organize source content, plan sentences, and prepare a system for automatic generation of original content.
• Understand the NLG pipeline and apply the right techniques at each stage.
• Understand the architecture of a Natural Language Generation (NLG) system.
• Implement the most suitable algorithms and models for analysis and ordering.
• Pull data from publicly available data sources as well as curated databases to use as material for generated text.
• Replace manual and laborious writing processes with computer-generated, automated content creation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Natural Language Generation (NLG)

• Overview of NLG and its applications
• Understanding the NLG pipeline
• Introduction to Python libraries for NLG

Data Collection and Preparation

• Collecting data from various sources
• Cleaning and preprocessing text data
• Organizing content for generation

Language Modeling for NLG

• Introduction to language models
• Training a language model for text generation
• Fine-tuning language models using SpaCy and NLTK

Sentence Planning and Text Structuring

• Planning sentence structure and content flow
• Using templates for text generation
• Customizing text structure based on use cases

Content Generation and Post-Processing

• Generating text from structured data
• Evaluating and refining generated content
• Post-processing and formatting output

Advanced NLG Techniques

• Using neural networks for text generation (e.g., GPT models)
• Handling context and coherence in generated text
• Exploring real-world applications and case studies

Final Project: Building an NLG System

• Defining a project scope
• Building and deploying an NLG system
• Testing and evaluating the system

Summary and Next Steps