Show HN: Natural Language Processing Demystified (Part One)

#1

Post by mothcamp » Wed, May 18, 2022, 10:55 AM UTC

Hi HN:

I published part one of my free NLP course. The course is intended to help anyone who knows Python and a bit of math go from the very basics all the way to today's mainstream models and frameworks.

I strive to balance theory and practice and so every module consists of detailed explanations and slides along with a Colab notebook (in most modules) putting the theory into practice.

In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up useful bits on how to use tools such as spaCy and scikit-learn.

No registration required: https://www.nlpdemystified.org/

Show HN: Natural Language Processing Demystified (Part One)
nlpdemystified.org

Re: Show HN: Natural Language Processing Demystified (Part One)

#2

Post by mothcamp » Wed, May 18, 2022, 10:56 AM UTC

Hi HN:

I published part one of my free NLP course. The course is intended to help anyone who knows Python and a bit of math go from the very basics all the way to today's mainstream models and frameworks.

I strive to balance theory and practice and so every module consists of detailed explanations and slides along with a Colab notebook (in most modules) putting the theory into practice.

In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up useful bits on how to use tools such as spaCy and scikit-learn.

No registration required: https://www.nlpdemystified.org/

Re: Show HN: Natural Language Processing Demystified (Part One)

#3

Post by jll29 » Wed, May 18, 2022, 11:31 AM UTC

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools).

But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in three lines of Python, but you will still not know much about what parts of speech are, which languages have which ones, what function they have in linguistic theory or practical applications.

What are the advantages of using the C7 tagset over the C5 or PENN tagsets?

Why is AT sometimes called DET?

etc.

I recommend people spend a bit of time to read an(y) introduction to linguistics textbook before diving into NLP, then the second investment will be worth so much more.

Re: Show HN: Natural Language Processing Demystified (Part One)

#4

Post by PainfullyNormal » Wed, May 18, 2022, 11:45 AM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

> I recommend people spend a bit of time to read an(y) introduction to linguistics textbook

Do you have a favorite you can recommend?

Re: Show HN: Natural Language Processing Demystified (Part One)

#5

Post by amitport » Wed, May 18, 2022, 11:47 AM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

NLP is a vast field nowadays, you can solve a research problem with a novel transformer architecture (for example) without knowing anything about linguistics. The thing is that NLP is a vast field and there is plenty room to go around (same goes for vision, you don't really need classical vision background as much as you used to).

(also an NLP researcher. Knows nothing about linguistics)

Re: Show HN: Natural Language Processing Demystified (Part One)

#6

Post by vb234 » Wed, May 18, 2022, 11:47 AM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

Could you recommend a good introduction to NLP book?

Re: Show HN: Natural Language Processing Demystified (Part One)

#7

Post by xtiansimon » Wed, May 18, 2022, 11:48 AM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

“I recommend people spend a bit of time to read an(y) introduction to linguistics textbook…”

Linguists is a broad area of study. Can you be more specific? Such as grammar and syntax.

Re: Show HN: Natural Language Processing Demystified (Part One)

#8

Post by philophyse » Wed, May 18, 2022, 12:01 PM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

In your opinion, would George Yule's The Study of Language be a good introduction to linguistics? Or is there any other book that you would recommend to someone who has little knowledge of the field, but a lot of interest?

Re: Show HN: Natural Language Processing Demystified (Part One)

#9

Post by mywaifuismeta » Wed, May 18, 2022, 12:06 PM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

I'm generally not a fan of these kind of high-level tutorials that tell you "use X library to get Y result" - it's just not good for learning. But any content that tries to sell you on learning ML/NLP/etc in a few weeks is just that. I understand people want to make money by targeting a large audience, but it makes me sad when I see (the vast vast majority) of practitioners not having any understanding about ML (or NLP) and just blindly applying libraries.

I don't think you necessarily need a linguistics background for NLP, but I think you need either a strong linguistics OR ML background so that you know what's going on under the hood and can make connections. Anyone can call into Huggingface, you don't need a course for that.

Re: Show HN: Natural Language Processing Demystified (Part One)

#10

Post by LunaSea » Wed, May 18, 2022, 12:09 PM UTC

jll29 wrote: post #3

NLP researcher here. It's great to see many offerings for courses and tutorials, and NLP has made a lot of progress, in terms of both its science as well as its re-usable software artifacts (ibraries & notebooks, standalone tools). But what saddens me is too many people are trying to dive into NLP without trying to understand language & linguistics first. For example, you can run a part of speech (POS) tagger in thre…

Is this still true in an era where most NLP problems use language models as a solution?