One can feel for the authors, it's such a struggle to write a textbook in a time when NeurIPS gets 20000 submissions and ACL has 6500 registered attendees (as of August '05), and every day, dozens of relevant ArXiv pre-prints appear.

Controversial opinion (certainly the publisher would disagree with me): I would not take out older material, but arrange it by properties like explanatory power/transparency/interpreability, generative capacity, robustness, computational efficiency, and memory footprint. For each machine learning method, an example NLP model/application could be shown to demonstrate it.

Naive Bayes is way too useful to downgrade it to an appendix position.

It may also make sense to divide the book into timeless material (Part I: what's a morphem? what's a word sense?) and (Part II:) methods and datasets that change every decade.

This is the broadest introductory book for beginners and a must-read; like the ACL family of conferences it is (nowadays) more of an NLP book (i.e., on engineering applications) than a computational linguistics (i.e., modeling/explaining how language-based communication works) book.