Book Image

Artificial Vision and Language Processing for Robotics

By : Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre
Book Image

Artificial Vision and Language Processing for Robotics

By: Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai Garay Maestre

Overview of this book

Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video. By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.
Table of Contents (12 chapters)
Artificial Vision and Language Processing for Robotics
Preface

Topic Modeling


Within NLU, which is a part of NLP, one of the many tasks that can be performed is extracting the meaning of a sentence, a paragraph, or a whole document. One approach to understanding a document is through its topics. For example, if a set of documents is from a newspaper, the topics might be politics or sports. With topic modeling techniques, we can obtain a bunch of words representing various topics. Depending on your set of documents, you will then have different topics represented by different words. The goal of these techniques is to know the different types of documents in your corpus.

Term Frequency – Inverse Document Frequency (TF-IDF)

TF-IDF is a commonly used NLP model for extracting the most important words from a document. To perform this classification, the algorithm will assign a weight to each word. The idea of this method is to ignore words without relevance to the meaning of a global concept, (which means the overall topic of a text), so those terms will be...