Natural Language Annotation for Machine Learning

Natural Language Annotation for Machine Learning

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Create your own natural language training corpus for machine learning. Whether youa€™re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cyclea€”the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You dona€™t need any programming or linguistics experience to get started. Using detailed examples at every step, youa€™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 Oa€™Reillya€™s Natural Language Processing with Python.More complicated cases emerge when we look at product reviews, or more nuanced reviews, where the text is ... Consider the following review, for example: I received my Kindle Fire this morning and it is pretty amazing. ... For example, following Liu (2012), we can define an opinion as a tuple consisting of the following elements: Opinion = alt;h, e, a, so, tagt; where h is an opinion holder; e is the target.

Title:Natural Language Annotation for Machine Learning
Author:James Pustejovsky, Amber Stubbs
Publisher:"O'Reilly Media, Inc." - 2012-10-11


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