James Pustejovsky,Amber Stubbs
Create your own natural language training corpus for machine learning. Whether youâ€™re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycleâ€”the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You donâ€™t need any programming or linguistics experience to get started.,Using detailed examples at every step, youâ€™ll learn how the ,MATTER Annotation Development Process, helps you ,M,odel, ,A,nnotate, ,T,rain, ,T,est, ,E,valuate, and ,R,evise 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 Oâ€™Reillyâ€™s ,Natural Language Processing with Python,.
Computer Science,Programming,Humanities,Linguistics,Nonfiction,Science,Technology
Natural Language Annotation for Machine Learning