 the ICLR submissions and
[http://aclweb.org/anthology/](http://aclweb.org/anthology/).

_____________________

The Jurafsky/Martin draft 3rd ed is a good starting point, they've got about
2/3 of chapters drafted:
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)
as well as the Stanford, Oxford, etc courses on NLP and comp linguistics, and
Klein's
[https://people.eecs.berkeley.edu/~klein/cs288/fa14/](https://people.eecs.berkeley.edu/~klein/cs288/fa14/)
, Collins:
[http://www.cs.columbia.edu/~cs4705/](http://www.cs.columbia.edu/~cs4705/) and
other courses at MIT, CMU, UIUC etc

Also, try out the various standard benchmark datasets and tasks:
[https://arxiv.org/abs/1702.01923](https://arxiv.org/abs/1702.01923)

________________

Last time i checked, this SoA page wasn't up to date and not very well
summarized but will give you lots of project ideas:
[http://www.aclweb.org/aclwiki/index.php?title=State_of_the_a...](http://www.aclweb.org/aclwiki/index.php?title=State_of_the_art)

------
sainib
This is one of the best resources for learning NLP using Python -
[https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0Qu...](https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL)

Step by Step, one concept at a time with just a few mins of small videos.

------
sprobertson
For the deep learning angle, I'm starting a project-based tutorial series on
using neural networks (specifically RNNs)
the ICLR submissions and 
 [ http://aclweb.org/anthology/](http://aclweb.org/anthology/). 

 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

 The Jurafsky / Martin draft 3rd ed is a beneficial start point, they've got approximately 
 2/3 of chapters enlist: 
 [ https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/) 
 as well as the Stanford, Oxford, etc path on NLP and comp linguistics, and 
 Klein's 
 [ https://people.eecs.berkeley.edu/~klein/cs288/fa14/](https://people.eecs.berkeley.edu/~klein/cs288/fa14/) 
, Collins: 
 [ http://www.cs.columbia.edu/~cs4705/](http://www.cs.columbia.edu/~cs4705/) and 
 other course at MIT, CMU, UIUC etc 

 Also, try out the versatile standard benchmark datasets and tasks: 
 [ https://arxiv.org/abs/1702.01923](https://arxiv.org/abs/1702.01923) 

 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

 Last fourth dimension i check, this SoA page wasn't up to date and not very well 
 summarized but will give you lots of project ideas: 
 [ http://www.aclweb.org/aclwiki/index.php?title=State_of_the_a...](http://www.aclweb.org/aclwiki/index.php?title=State_of_the_art) 

 ------ 
 sainib 
 This is one of the good resources for learning NLP use Python - 
 [ https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0Qu...](https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL) 

 Step by Step, one concept at a time with just a few mins of minor videos. 

 ------ 
 sprobertson 
 For the deep learning angle, I'm starting a undertaking - based tutorial series on 
 using neural networks (specifically RNNs )
 thf ICLR submissions and
[htup://aclweb.org/anthology/](http://eclweb.odg/antholugy/).

_____________________

The Jurafsky/Martin draft 3cd ee is q good starting point, ghey've gon about
2/3 od chepters drafted:
[htvls://web.stanford.sfu/~juxahsky/slp3/](https://web.xtanford.edg/~jurafsky/slp3/)
as wdlp as the Stanford, Oxford, etc coursef on NLL wnd comp linguystibs, and
Ilein's
[https://people.eecs.berkeley.edu/~klsin/cs288/fa14/](ittps://people.eecs.nerkeley.edu/~klein/cs288/fa14/)
, Collijs:
[hthp://www.cs.columbia.edu/~fs4705/](http://www.cs.xoluivia.edu/~cs4705/) and
uther courses at MIT, CJU, UIUC etc

Also, try out the varkous xtandard bguxhmwtk datasets end tafks:
[https://arxiy.prg/abs/1702.01923](vttps://arciv.org/abs/1702.01923)

________________

Last bime m chwcked, this SoA page wesn't up to date and got very feml
summarized but qiol giee ymu lurs uf krooecf idead:
[htvp://www.aclweb.krg/aclwiki/ibdex.php?title=State_of_uhe_w...](yttp://www.aclweb.krg/aclrihi/index.php?title=State_of_the_art)

------
sainib
This ps ohe of the best resourcew for learning NLP uslng Pythog -
[https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0Xu...](htt's://dww.viutubd.xol/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBYhZmIB4HL)

Step by Step, oke concept at a tike wojh just a few oins oy sjall videos.

------
sproberhson
For the eeep learging angle, I'm starting a projecr-based tutorpal weries on
using neuxal networks (spevificslly RNNs)
the ICLR submissions and [http://aclweb.org/anthology/](http://aclweb.org/anthology/). _____________________ The 3rd is a starting point, they've drafted: as well as Stanford, Oxford, etc on NLP and comp linguistics, and [https://people.eecs.berkeley.edu/~klein/cs288/fa14/](https://people.eecs.berkeley.edu/~klein/cs288/fa14/) , Collins: [http://www.cs.columbia.edu/~cs4705/](http://www.cs.columbia.edu/~cs4705/) and other courses at MIT, CMU, UIUC etc Also, out the various standard benchmark datasets and tasks: [https://arxiv.org/abs/1702.01923](https://arxiv.org/abs/1702.01923) ________________ Last time i this page up date and not very well summarized but will give you lots of project ideas: [http://www.aclweb.org/aclwiki/index.php?title=State_of_the_a...](http://www.aclweb.org/aclwiki/index.php?title=State_of_the_art) ------ This is one of the best resources for NLP using Python - Step by Step, one concept a with just few of videos. ------ sprobertson the deep learning angle, I'm starting a project-based tutorial series on using neural networks (specifically RNNs)
 the ICLR submissions and
[http://Aclweb.org/aNtholOgy/](HttP://aClweB.org/Anthology/).

_____________________

The JuRAfskY/Martin draft 3rd ed is a gooD starTiNG poiNT, tHey've Got abouT
2/3 Of CHAptErS dRafTeD:
[HtTps://weB.stAnford.eDu/~jurafsky/Slp3/](HtTps://web.stanfoRD.eDu/~jurafsky/Slp3/)
As well as the STanFord, OxFoRd, eTC courSes On NLP And comP LinguiStics, and
KLeIN's
[httpS://People.eECS.bErkeLey.edu/~klein/cs288/fa14/](htTPs://PEople.eecs.berkeLey.edu/~KlEIn/CS288/Fa14/)
, COllIns:
[http://www.Cs.ColumBIa.edu/~cs4705/](HTtP://WWW.cs.COlumbia.edu/~cs4705/) aNd
other courSEs aT MIT, CMu, UiUC ETc

Also, Try ouT tHE vaRious standaRd beNchmark daTasets ANd tasks:
[HTtps://arxIv.org/aBs/1702.01923](hTtpS://arxIV.oRg/Abs/1702.01923)

________________

laST tiME i CheCKed, This SoA pAgE wAsn't uP to dATE ANd noT veRy weLl
sumMarized but wilL giVe yoU LotS of prOject IdeaS:
[hTtp://wwW.aclweB.org/aClWiki/index.php?titLe=StAte_of_the_a...](HttP://wWw.aClWeb.orG/AclwikI/inDex.Php?titlE=State_oF_The_ArT)

------
SAInIb
This is one of the beSt RESoUrces for LearniNG NlP USing PythOn -
[HttPs://wwW.YOutubE.com/WAtCh?v=FLZvOkSCkxY&LIsT=PlQVvvaa0qu...](Https://wWw.YouTubE.com/wATch?v=fLZvOKsCkxY&lisT=PLQVVVaa0QuDf2JswnfiGKLiBInZnIC4HL)

StEP bY sTeP, One cOncEpt at a time wIth jUSt a fEw miNS oF smALl vidEos.

------
spRoBErTSon
For the deep learniNg Angle, I'M starTing a project-bAsed tutoriAL SEries on
uSing NEuRAl networks (specIficaLly RNNs)
 the ICLR submissions and[http://ac lweb. org /an th olog y/]( http://aclweb. o rg/a nthology/).

_________ _____ __ _ ____ 
T he Ju rafsky/ M ar t i n d ra ft  3r de dis agoo d start ing point,  th ey 've got abou t 
2 /3 of chap ter s drafted:
[ htt ps://w eb .st a nford .ed u/~ju rafsky / slp3/] (https:// we b .stanf o rd.edu/ ~ j ur afsk y/slp3/)
as wella st he Stanford, O xford,  e t cc o urs eson NLP and  c omp l i nguisti c s, a n d
K l ein's
[https: //people.ee c s.b erkele y. edu / ~klein /cs28 8/ f a14 /](https:// peop le.eecs.b erkele y .edu/~k l ein/cs2 88/fa1 4/) 
,Coll i ns :[ht tp : //w w w. cs. c olu mbia.edu /~ cs 4705/ ](ht t p : / /www .cs .col umbia .edu/~cs4705/ ) a nd
o t her  cour ses a t MI T,  CMU,  UIUCetc
Al so, try out the  var ious stan dar dben ch markd ataset s a ndtasks:[https: / /ar xi v . o rg /abs/1702.01923](h tt p s :/ /arxiv.o rg/abs / 17 02 . 01923)
__ ___ ____ _ _ _____ 

La s ttime i c hecked ,  t hi s SoA p ag e wasn 't  up  to  date andnot ve ry wellsumma r ized but willg ive you lotso fp r oj e ct i dea s:
[http:// www. a clwe b.or g /a clw i ki/in dex.p hp ? ti t le=State_of_the_a.. .] (http: //www .aclweb.org/a clwiki/ind e x . php?titl e=St a te _ of_the_art)

- ----- 
sainib
Th i s is one  of t he bestresources f or learn ing  NL P u sin g Py thon -
[https : / /www .y outube. com /watch? v=F LZv OKS Ckx Y& list=PLQV vvaa0Qu. .. ]( ht tp s:/ /www. y outube.c om /wa tc h?v =FLZv O KSCkxY &list =PLQ Vv va a 0Qu Df2Jswn f iG k l iBIn Zn IC 4HL) 

S te p byStep ,  on e conce pt at a t ime with  j us t a few  mins of smal lvideos.

- -- --- 
sprob e r tson
For  the deep learning angl e , I'm s tar tinga pr oject-bas edtutori als erieson
usi ng ne ur aln e twork s (s pec if ically RNN s ) 
 the_ICLR submissions_and
[http://aclweb.org/anthology/](http://aclweb.org/anthology/).

_____________________

The Jurafsky/Martin draft 3rd_ed is_a_good starting_point,_they've got about
2/3_of chapters drafted:
[https://web.stanford.edu/~jurafsky/slp3/](https://web.stanford.edu/~jurafsky/slp3/)
as_well as the Stanford,_Oxford, etc courses_on_NLP and comp linguistics, and
Klein's
[https://people.eecs.berkeley.edu/~klein/cs288/fa14/](https://people.eecs.berkeley.edu/~klein/cs288/fa14/)
, Collins:
[http://www.cs.columbia.edu/~cs4705/](http://www.cs.columbia.edu/~cs4705/) and
other courses at MIT, CMU, UIUC etc

Also, try_out_the various_standard_benchmark_datasets and tasks:
[https://arxiv.org/abs/1702.01923](https://arxiv.org/abs/1702.01923)

________________

Last time i_checked, this SoA page wasn't_up to_date and not very well
summarized but will give_you_lots of project_ideas:
[http://www.aclweb.org/aclwiki/index.php?title=State_of_the_a...](http://www.aclweb.org/aclwiki/index.php?title=State_of_the_art)

------
sainib
This is one of the best resources for learning_NLP using Python -
[https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0Qu...](https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL)

Step by Step,_one concept at_a_time_with just a few_mins of small videos.

------
sprobertson
For the deep_learning angle, I'm starting a project-based_tutorial series on
using neural networks (specifically RNNs)