vkhuc

Absolutely. This dataset search was first introduced in September of 2018. It was out of beta in January last year: https://www.kdnuggets.com/2020/01/google-dataset-search.html.

Thanks. That's the book I'm reading :)

Although I'm working on deep neural nets, this material is too advanced to me. Looks like deep nets + bayesian reasoning is the next big thing.

Why use NLTK when we can use spaCy instead? http://spacy.io/

Nice. As a guy who got involved into OpenNLP's development, I'd like to see the comparison too.

Also, it would be great if you include SENNA into your benchmark: http://ml.nec-labs.com/senna/

I think they used the methods described in http://www.cs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf

I've been looking at Factorie for a while but haven't actually done anything heavy with it.

I planned to replace the optimizer in CMU's POS tagger with the one implemented in OpenNLP to make the tagger fully Apache. Unfortunately, so busy right now. Currently, I'm running the tagger on AWS, so the GPL doesn't hurt me much.

BTW, besides the POS tagger, CMU's TweeboParser depends on Turbo Parser which again is licensed under GPL.

Yes, Noah Smith's NLP group at CMU is awesome. Btw, Noah is moving to UWash.

The whole project is based on various libraries. In particular, the POS tagger itself uses the OWLQN optimizer from Stanford NLP (licensed under GPL).

However, it's possible to remove GPL libraries out of the POS tagger as mentioned here: https://github.com/brendano/ark-tweet-nlp/blob/master/LICENS...

Great, I've been waiting for the release of TweeboParser. Just before EMNLP14 gets started.

There are some (free) good books that haven't been mentioned yet:

1) "Data Mining and Analysis: Fundamental Concepts and Algorithms" by Zaki and Meira http://www.cs.rpi.edu/~zaki/PaperDir/DMABOOK.pdf

This book covers many ML topics with concrete examples.

2) "Computer Vision: Models, Learning, and Inference" by Simon Prince: http://web4.cs.ucl.ac.uk/staff/s.prince/book/book.pdf

Despite a CV book, the first half of it is like a statistics book that comes with examples in CV which are very easy to follow.

I skimmed over Mohri's book and I think the topics it covers are quite narrow.

For mathematical foundations of ML, I would recommend the book "Understanding Machine Learning: From Theory to Algorithms" by Shai Shalev-Shwartz.

A brief version of the book is available to download on the author's website: http://www.cs.huji.ac.il/~shais/Handouts.pdf

If you prefer C, you can try SENNA: http://ml.nec-labs.com/senna/. It includes POS tagging, chunking, NER, constituency parsing and semantic role labeling. But dependency parsing is not there yet.

It's super fast (thanks to C) and very accurate (thanks to Deep Learning approach). The license is not for commercial usage though.

SENNA can be used with NLTK: http://pydoc.net/Python/nltk/2.0.2/nltk.tag.senna/

Besides Julia, I think another alternate language to Python for scientific computation would be Scala. Breeze (from ScalaNLP project) is an effort to bring Numpy and Matlab syntax to Scala: https://github.com/scalanlp/breeze/wiki/Breeze-Linear-Algebr...

The learning curve for Scala may be steep though.

I think the book "Speech and Language Processing, 2nd Edition" from Prof. Daniel Jurafsky is very good http://www.amazon.com/Speech-Language-Processing-Daniel-Jura...

You can also check out the great online NLP course taught by the author and Prof. Chris Manning from Stanford: https://www.youtube.com/watch?v=nfoudtpBV68&list=PL6397E4B26...