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The Natural Language Processing Workshop

You're reading from  The Natural Language Processing Workshop

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781800208421
Pages 452 pages
Edition 1st Edition
Languages
Authors (6):
Rohan Chopra Rohan Chopra
Profile icon Rohan Chopra
Aniruddha M. Godbole Aniruddha M. Godbole
Profile icon Aniruddha M. Godbole
Nipun Sadvilkar Nipun Sadvilkar
Profile icon Nipun Sadvilkar
Muzaffar Bashir Shah Muzaffar Bashir Shah
Profile icon Muzaffar Bashir Shah
Sohom Ghosh Sohom Ghosh
Profile icon Sohom Ghosh
Dwight Gunning Dwight Gunning
Profile icon Dwight Gunning
View More author details

Table of Contents (10) Chapters

Preface
1. Introduction to Natural Language Processing 2. Feature Extraction Methods 3. Developing a Text Classifier 4. Collecting Text Data with Web Scraping and APIs 5. Topic Modeling 6. Vector Representation 7. Text Generation and Summarization 8. Sentiment Analysis Appendix

What Is a Vector?

The basic mathematical definition of a vector is an object that has both magnitude and direction. In our definition, it is mostly compared with a scalar, which can be defined as an object that has only magnitude. Vectors are also defined as an element in vector space—for example, a point in space with the coordinates (x=4, y=5, z=6) is a vector. Here, we can see the vector dimensions are the geometric coordinates of a point or element in space. However, the vector dimensions can also represent any quantity or property of some element or object in addition to mere geometric coordinates.

As an example, let's say that we're defining the weather at a given place using five features: temperature, humidity, precipitation, wind speed, and air pressure. The units that these would be measured in are Celsius, percentage, centimeters, kilometers per hour (km/h), and millibar (mbar), respectively. The following are the values for two places:

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