Abstract
In Chapters 2 and 4 we discussed classifiers that accept feature vectors as input, without getting into much details about the contents of these vectors. In Chapters 6 and 7 we discussed the sources of information which can serve as the core features for various natural language tasks. In this chapter, we discuss the details of going from a list of core-features to a feature-vector that can serve as an input to a classifier.
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Goldberg, Y. (2017). From Textual Features to Inputs. In: Neural Network Methods for Natural Language Processing. Synthesis Lectures on Human Language Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-02165-7_8
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DOI: https://doi.org/10.1007/978-3-031-02165-7_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-01037-8
Online ISBN: 978-3-031-02165-7
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