This book is intended as a resource for people who are interested in using computers to help process natural language. A natural language refers to any language spoken by humans, either currently (e.g., English, Chinese, Spanish) or in the past (e.g., Latin, ancient Greek, Sanskrit). Annotation refers to the process of adding metadata information to the text in order to augment a computer’s capability to perform Natural Language Processing (NLP). In particular, we examine how information can be added to natural language text through annotation in order to increase the performance of machine learning algorithms—computer programs designed to extrapolate rules from the information provided over texts in order to apply those rules to unannotated texts later on.
Natural Language Annotation for Machine Learning
This book details the multistage process for building your own annotated natural language dataset (known as a corpus) in order to train machine learning (ML) algorithms for language-based data and knowledge discovery. The overall goal of this book is to show readers how to create their own corpus, starting with selecting an annotation task, creating the annotation specification, designing the guidelines, creating a “gold standard” corpus, and then beginning the actual data creation with the annotation process.
Because the annotation process is not linear, multiple iterations can be required for defining the tasks, annotations, and evaluations, in order to achieve the best results for a particular goal. The process can be summed up in terms of the MATTER Annotation Development Process: Model, Annotate, Train, Test, Evaluate, Revise. This book guides the reader through the cycle, and provides detailed examples and discussion for different types of annotation tasks throughout. These tasks are examined in depth to provide context for readers and to help provide a foundation for their own ML goals.
Additionally, this book provides access to and usage guidelines for lightweight, user-friendly software that can be used for annotating texts and adjudicating the annotations. While a variety of annotation tools are available to the community, the Multipurpose Annotation Environment (MAE) adopted in this book (and available to readers as a free download) was specifically designed to be easy to set up and get running, so that confusing documentation would not distract readers from their goals. MAE is paired with the Multidocument Adjudication Interface (MAI), a tool that allows for quick comparison of annotated documents.
This book is written for anyone interested in using computers to explore aspects of the information content conveyed by natural language. It is not necessary to have a programming or linguistics background to use this book, although a basic understanding of a scripting language such as Python can make the MATTER cycle easier to follow, and some sample Python code is provided in the book. If you don’t have any Python experience, we highly recommend Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper (O’Reilly), which provides an excellent introduction both to Python and to aspects of NLP that are not addressed in this book.
It is helpful to have a basic understanding of markup languages such as XML (or even HTML) in order to get the most out of this book. While one doesn’t need to be an expert in the theory behind an XML schema, most annotation projects use some form of XML to encode the tags, and therefore we use that standard in this book when providing annotation examples. Although you don’t need to be a web designer to understand the book, it does help to have a working knowledge of tags and attributes in order to understand how an idea for an annotation gets implemented.
Organization of This Book
Chapter 1 of this book provides a brief overview of the history of annotation and machine learning, as well as short discussions of some of the different ways that annotation tasks have been used to investigate different layers of linguistic research. The rest of the book guides the reader through the MATTER cycle, from tips on creating a reasonable annotation goal in Chapter 2, all the way through evaluating the results of the annotation and ML stages, as well as a discussion of revising your project and reporting on your work in Chapter 9. The last two chapters give a complete walkthrough of a single annotation project and how it was recreated with machine learning and rule-based algorithms. Appendixes at the back of the book provide lists of resources that readers will find useful for their own annotation tasks.
While it’s possible to work through this book without running any of the code examples provided, we do recommend having at least the Natural Language Toolkit (NLTK) installed for easy reference to some of the ML techniques discussed. The NLTK currently runs on Python versions from 2.4 to 2.7. (Python 3.0 is not supported at the time of this writing.) For more information, see http://www.nltk.org.
The code examples in this book are written as though they are in the
interactive Python shell programming environment. For information on how
to use this environment, please see: http://docs.python.org/tutorial/interpreter.html.
If not specifically stated in the examples, it should be assumed that the
import nltk was used prior to all sample
Conventions Used in This Book
The following typographical conventions are used in this book:
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Using Code Examples
This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Natural Language Annotation for Machine Learning by James Pustejovsky and Amber Stubbs (O’Reilly). Copyright 2013 James Pustejovsky and Amber Stubbs, 978-1-449-30666-3.”
If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at firstname.lastname@example.org.
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We would like thank everyone at O’Reilly who helped us create this book, in particular Meghan Blanchette, Julie Steele, Sarah Schneider, Kristen Borg, Audrey Doyle, and everyone else who helped to guide us through the process of producing it. We would also like to thank the students who participated in the Brandeis COSI 216 class during the spring 2011 semester for bearing with us as we worked through the MATTER cycle with them: Karina Baeza Grossmann-Siegert, Elizabeth Baran, Bensiin Borukhov, Nicholas Botchan, Richard Brutti, Olga Cherenina, Russell Entrikin, Livnat Herzig, Sophie Kushkuley, Theodore Margolis, Alexandra Nunes, Lin Pan, Batia Snir, John Vogel, and Yaqin Yang.
We would also like to thank our technical reviewers, who provided us with such excellent feedback: Arvind S. Gautam, Catherine Havasi, Anna Rumshisky, and Ben Wellner, as well as everyone who read the Early Release version of the book and let us know that we were going in the right direction.
We would like to thank members of the ISO community with whom we have discussed portions of the material in this book: Kiyong Lee, Harry Bunt, Nancy Ide, Nicoletta Calzolari, Bran Boguraev, Annie Zaenen, and Laurent Romary.
Additional thanks to the members of the Brandeis Computer Science and Linguistics departments, who listened to us brainstorm, kept us encouraged, and made sure everything kept running while we were writing, especially Marc Verhagen, Lotus Goldberg, Jessica Moszkowicz, and Alex Plotnick.
This book could not exist without everyone in the linguistics and computational linguistics communities who have created corpora and annotations, and, more importantly, shared their experiences with the rest of the research community.
I would like to thank my wife, Cathie, for her patience and support during this project. I would also like to thank my children, Zac and Sophie, for putting up with me while the book was being finished. And thanks, Amber, for taking on this crazy effort with me.
I would like to thank my husband, BJ, for encouraging me to undertake this project and for his patience while I worked through it. Thanks also to my family, especially my parents, for their enthusiasm toward this book. And, of course, thanks to my advisor and coauthor, James, for having this crazy idea in the first place.