Speech and Language Processing, 3rd ed. draft (2017)

On the slim chance that someone here wants to re-implement the unmodified Kneser-Ney algorithm[0], the presentation of it by the book does not account for unknown tokens in the query not in the vocabulary. I extended the recurrence to its natural closure including unknown tokens here [https://github.com/jhanschoo/HMMTagger/blob/master/readme.pd...]. A straightforward task, but it might take you an hour or two (probably more) otherwise to obtain it and prove its correctness, seeing as I couldn't find an extension…

I almost believe that if I know how make an LLM prompt and how to make an API call to OpenAI, Mistral, Claude 3, or together.ai, then as an application programmer, I can skip this whole book. I see people posting project specifications asking for NLP, named entity extraction, etc. But most things in those jobs look like they could be handled by an LLM, possibly even smaller than 7b, and probably more robustly. My other assumption is that this…

I used the 2nd edition of this textbook in my undergraduate studies extensively (linguistics). Coming from a non-technical background and starting to take technical classes, certain chapters were wonderful ways for me to bridge that gap. Specifically the second chapter on text normalisation helped me apply things I’d learned in 100 and 200 level classes and ultimately set me on the path to becoming an engineer. And I still use that text processing knowledge a lot in my day to…

I learned speech recognition from the 2nd edition of Jurafsky's book (2008). The field has changed so much it sometimes feels unrecognizable. Instead of hidden markov models, gaussian mixture models, tri-phone state trees, finite state transducers, and so on, nearly the whole stack has been eaten from the inside out by neural networks. But, there's benefit to the fact that deep learning is now the "lingua franca" across machine learning fields. In 2008, I would have struggled to usefully share…