Reddit reviews Speech and Language Processing, 2nd Edition
We found 14 Reddit comments about Speech and Language Processing, 2nd Edition. Here are the top ones, ranked by their Reddit score.
I think the field you are looking for is called Natural Language Processing.
There is a nice introductory lecture on it on coursera.
I think this is the standard introduction book.
This is the standard introductory book to the field: http://www.amazon.co.uk/Language-Processing-Prentice-Artificial-Intelligence/dp/0131873210
In Natural Language Processing, it's Jurafsky and Martin. In Machine Learning, it's debatably the Bishop book.
Start with Jurafsky and Martin to get a rounded overview of the main problems and approaches. I don't use NLTK myself, but it has a large community around it and some decent tutorials I hear.
I would repeat jbu311's point that your interests are way too broad. If you're interested in going into depth in anything, you'll have to pick a topic. Even the ones you mentioned here are fairly broad (and I'm not sure what you meant about concurrency and parallelization "underscoring" AI?).
If you want to learn about the field of natural language processing, which is a subfield of AI, I would suggest Jurafsky and Martin's new book. If you're interested more broadly in AI and can't pick a topic, you might want to check out Russell & Norvig (although you might also want to wait a few months for the third edition).
Soeech and Language Processing is often considered to be a good introductory text to NLP regardless of which side you come from (linguistics or maths/CS), and thus should provide enough information about linguistic theory to be sufficient for doing most of the standard NLP tasks.
If you would prefer a pure linguistics book, there are many good options available. Contemporary Linguistic Analysis is a solid introductory textbook used in intro ling classes (and have used it myself to teach before).
You might also wish to read something more specific depending on what kind of language processing you end focusing on, but I think a general fundamental understanding of ideas in linguistics would help a lot. Indeed as you are probably aware, less and less of modern NLP uses ideas from linguistics in favour of data-driven approaches, so having a substantial linguistics background is often not necessary.
Sorry for only having a small number of examples - just the first two that came to my head. Let me know if you would like some more options and I can see what else I can think of.
Edit: missed some words
Have you heard of this thing called Natural Language Processing?
You too can learn how to use NLP to analyze text quickly with computers. Start by reading a book like this or this, then solve practice problems like these.
You, too, can learn how to process a corpus of 650,000 emails in 8 days!
From what I understand, programs like the University of Arizona's Master of Science in Human Language Technology have pretty good job placement records, and a lot of NLP industry jobs seem to bring in good money, so I don't think it would be a bad idea if it's something you're interested in.
As for books, one of the canonical texts in NLP seems to be Jurafsky and Martin's Speech and Language Processing. It's written in such a way as to serve as an intro to computer science for linguists and as an intro to linguistics for computer scientists.
It's nearing being 10 years old, so some more modern approaches, especially neural networks, aren't really covered, iirc (I don't have my copy with me here to check).
Really, it's a pretty nice textbook, and I think it can be had fairly cheap if you can find an international version.
The NLTK book is a good hands-on free introduction that doesn't require you to understand a whole lot of math.
Other than that, the "big two" textbooks are:
They're a lot more math-heavy than the NLTK book, but if you're interested in computational linguistics/NLP, then your math and statistics should be solid.
Honorable mention:
Hope this helps!
I always thought this was a pretty good introduction to UIMA.
http://www.morganclaypool.com/doi/abs/10.2200/S00194ED1V01Y200905HLT003
It presumes you know a bit about NLP already, and for that Jurafsky and Martin is a great place to start.
http://www.amazon.com/Speech-Language-Processing-2nd-Edition/dp/0131873210
There are some very nice video lectures from Chris Manning and Dan Jurafsky as well :
https://www.youtube.com/playlist?list=PLSdqH1MKcUS7_bdyrIl626KsJoVWmS7Fk
Well, I'm a bit late. But what /u/Liz_Me and /u/robthablob are saying is the same I was taught in NLP classes. DFA (Deterministic Finite Automatons) can be represented as regular expressions and vice versa. I guess you could tokenize without explicitly using either (e.g. split string at whitespace, although I suspect, and please correct me if I'm wrong, that this can also be represented as a DFA). The problem with this approach is that word boundaries don't always match whitespaces (e.g. periods or exclamation marks after last word of sentence). So I'd suggest, if you are working in NLP, that you become very familiar with regular expressions. Not only are they very powerful, but you'll also need to use them for other typical NLP tasks like chunking. Have a look at the chapter dedicated to the topic in Jurafsky and Martin's Speech and Language Processing (one of the standard NLP books) or Mastering Regular Expressions.
Ah, that makes sense. Yup, using any sort of large corpus like that to create a more general document space should help.
I don't know what the best way to visualize the data is. That's actually one of the big challenges with high dimensional vector spaces like this. Once you've got more than three bases you can't really draw it directly. One thing I have played around with is using D3.js to create a force directed graph where the distance between nodes corresponds to the distance between vectors. It wasn't super helpful though. However I just went to look at some D3.js examples and it looks like there's an example of an adjacency matrix here: https://bost.ocks.org/mike/miserables/ I've never used one, but it seems like it could be helpful.
The link seems to working now for me, but if it stops working again here's the book it was taken from: https://www.amazon.com/Speech-Language-Processing-Daniel-Jurafsky/dp/0131873210 googling the title should help you find some relevant PDFs.
Di editori ce ne sono diversi, se cerchi un'introduzione alla PNL con un approccio accademico, ti consiglio: Speech And Language Processing: An Introduction to Natural Language Processing , Computational Linguistics, and Speech Recognition.
Alternativamente, Natural Language Processing with Python .
Which toolkit are you using for your HMMs? The HTK book has some general steps on what to do when it comes to HMM-base ASR. You might also want to have a look at the Speech Recognition chapter in Jurafsky and Martin's Speech and Language Processing, if you can find it online or in a library.
That being said, the state-of-the-art for ASR is mostly DNNs. HMMs are being phased out quite quickly as the main acoustic models in most speech applications. If you're interested in speech, why not start with those?