Hi HN:<p>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.<p>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.<p>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.<p>No registration required: <a href="https://www.nlpdemystified.org/" rel="nofollow">https://www.nlpdemystified.org/</a>
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).<p>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.<p>What are the advantages of using the C7 tagset over the C5 or PENN tagsets?<p>Why is AT sometimes called DET?<p>etc.<p>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.
Hi HN:<p>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.<p>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.<p>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.<p>No registration required: <a href="https://www.nlpdemystified.org/" rel="nofollow">https://www.nlpdemystified.org/</a>
I'm working on extracting facts from sentences, see <a href="https://lxagi.com" rel="nofollow">https://lxagi.com</a>.<p>Which are the toughest NLP problems you know of that aren't being solved satisfactorily?