Natural Language Annotation for Machine Learning
James Pustejovsky, Amber Stubbs
Format: PDF / Kindle (mobi) / ePub
Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started.
Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.
- Define a clear annotation goal before collecting your dataset (corpus)
- Learn tools for analyzing the linguistic content of your corpus
- Build a model and specification for your annotation project
- Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework
- Create a gold standard corpus that can be used to train and test ML algorithms
- Select the ML algorithms that will process your annotated data
- Evaluate the test results and revise your annotation task
- Learn how to use lightweight software for annotating texts and adjudicating the annotations
This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.
on any operating system, open a terminal and navigate to the directory where the .jar file exists, then run this command: java -jar MAIversion.jar On most platforms, it is also possible to open the program by double-clicking on the .jar file. However, doing so will not allow all error messages to be displayed, so using the terminal is recommended. On all systems, you should see the window shown in Figure D-1. Figure D-1. MAI with a DTD loaded Loading Tasks and Files Loading a
evaluation metrics relationship tags, Linked Extent Annotation: Semantic Roles reporting, Reporting About Your Work–About Your Revisions annotation, About Your Annotation Task and Annotators annotators, About Your Annotation Task and Annotators corpus, About Your Corpus final test scores, Final Testing Scores ML Algorithm, About Your ML Algorithm model, About Your Model and Specifications on revisions, About Your Revisions specification, About Your Model and
increase the amount of work done by the annotators for no obvious benefit. Figure 4-1 shows the different levels of the hierarchy we are discussing. The top two levels are too vague, while the bottom is too specific to be useful. The third level is just right for this task. We face the same dichotomy when examining the list of semantic roles. The list given in linguistic textbooks is a very general list of roles that can be applied to the nouns in a sentence, but any annotation task trying to
discrete units (segments) in the language, and how they are interpreted. Phonetics The study of the sounds of human speech, and how they are made and perceived. A phoneme is the term for an individual sound, and is essentially the smallest unit of human speech. Lexicon The study of the words and phrases used in a language, that is, a language’s vocabulary. Discourse analysis The study of exchanges of information, usually in the form of conversations, and particularly the flow of
might have made a mistake that can be fixed later. Most important, the more information you share about your task, the more useful your task will be to other people, even if they don’t always agree with the decisions you made. Chapter 10. Annotation: TimeML Thus far in this book, we have been using TimeML as an example for annotation and machine learning (ML) tasks. In this chapter, we will discuss the development of TimeML as an annotation task, and guide you through the MAMA cycle,