Natural Language Annotation for Machine Learning

Chapter 1. The Basics

It seems as though every day there are new and exciting problems that people have taught computers to solve, from how to win at chess or Jeopardy to determining shortest-path driving directions. But there are still many tasks that computers cannot perform, particularly in the realm of understanding human language. Statistical methods have proven to be an effective way to approach these problems, but machine learning (ML) techniques often work better when the algorithms are provided with pointers to what is relevant about a dataset, rather than just massive amounts of data. When discussing natural language, these pointers often come in the form of annotations—metadata that provides additional information about the text. However, in order to teach a computer effectively, it’s important to give it the right data, and for it to have enough data to learn from. The purpose of this book is to provide you with the tools to create good data for your own ML task. In this chapter we will cover:

• Why annotation is an important tool for linguists and computer scientists alike

• How corpus linguistics became the field that it is today

• The different areas of linguistics and how they relate to annotation and ML tasks

• What a corpus is, and what makes a corpus balanced

• How some classic ML problems are represented with annotations

• The basics of the annotation development cycle

The Importance of Language Annotation

Everyone knows that the Internet is an amazing resource for all sorts ...