-
processing/)

~~~
navyad
Didn't know of this, highly helpful, thanks.

------
haidrali
Keep reading and practice with this book
[http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/), when you will
complete this book you will have a good understanding of NLP. Sample product
to work on suggestion would include

Implementing a classifier, For detail of it you can look at 13 chapter of
[http://nlp.stanford.edu/IR-
book/pdf/irbookonlinereading.pdf](http://nlp.stanford.edu/IR-
book/pdf/irbookonlinereading.pdf)

Cover topics like Sentiment analysis, Document Summarisation etc

~~~
tu7001
The information retrieval book is great lecture, I'm going through this and
implement algorithms, learn a lot.

~~~
haidrali
I have implemented these two algorithms back in 2013 do check it out
[https://github.com/wonderer007/Naive-Bayes-
classifier](https://github.com/wonderer007/Naive-Bayes-classifier)

------
kyrre
no point wasting your time on nltk:

cs224d (videos, lecture notes, assignments)

a similar course: [https://github.com/oxford-cs-
deepnlp-2017/lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)

good paper: [https://arxiv.org/abs/1103.0398](https://arxiv.org/abs/1103.0398)
"Natural Language Processing (almost) from Scratch"

------
gtani
I think you want to understand comp linguistics viewpoint: parsers, PoS
taggin, dependency analysis, syntax trees;

and the machine learning perspective: embeddings in, say, 100-200 dimensional
space (word2vec, glove) and topic modelling/LDA, and latent semantic analysis
from the 90's. Then you can read about inputting embedding datasets into LSTM,
GRU, content addressable memory/attention mechanisms etc that are being
furiously introduced (you can scan
- 
 processing/) 

 ~~~ 
 navyad 
 Didn't know of this, highly helpful, thanks. 

 ------ 
 haidrali 
 Keep reading and practice with this ledger 
 [ http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/), when you will 
 dispatch this book you will have a beneficial sympathy of NLP. Sample product 
 to sour on hypnotism would include 

 implement a classifier, For detail of it you can look at 13 chapter of 
 [ http://nlp.stanford.edu/IR- 
 koran / pdf / irbookonlinereading.pdf](http://nlp.stanford.edu / IR- 
 book / pdf / irbookonlinereading.pdf) 

 Cover topic like opinion analysis, Document Summarisation etc 

 ~~~ 
 tu7001 
 The information recovery book is big lecture, I'm go through this and 
 implement algorithms, learn a draw. 

 ~~~ 
 haidrali 
 I have implemented these two algorithms back in 2013 do check it out 
 [ https://github.com/wonderer007/Naive-Bayes- 
 classifier](https://github.com / wonderer007 / Naive - Bayes - classifier) 

 ------ 
 kyrre 
 no point waste your time on nltk: 

 cs224d (videos, lecture notes, assignments) 

 a similar path: [ https://github.com/oxford-cs- 
 deepnlp-2017 / lectures](https://github.com / oxford - cs - deepnlp-2017 / lectures) 

 good paper: [ https://arxiv.org/abs/1103.0398](https://arxiv.org/abs/1103.0398) 
 " Natural Language Processing (almost) from Scratch " 

 ------ 
 gtani 
 I think you want to understand comp linguistics viewpoint: parsers, PoS 
 taggin, dependency analysis, syntax trees; 

 and the machine learning perspective: embeddings in, say, 100 - 200 dimensional 
 space (word2vec, baseball glove) and topic modelling / LDA, and latent semantic psychoanalysis 
 from the 90's. Then you can understand about inputting embed datasets into LSTM, 
 GRU, content addressable memory / attention mechanism etc that are being 
 furiously introduce (you can scan
-
professing/)

~~~
navyad
Didn't know uf this, highly kwlpful, thanka.

------
haidralk
Keep reading and practice wmth rhis vook
[http://www.nltk.org/book_1dd/](http://www.jltk.org/biok_1ev/), when you will
complete bkis bklk ymn will have a gpod undersdanding of NLP. Sxm'le product
to work on suggestion woujd inclidf

Implementing w clsfsifjvr, For detail of it you can looi at 13 ciapter of
[http://nll.stanford.edu/IR-
book/pdf/irboononllnereading.pdf](http://npp.stanford.eeu/IR-
fiok/pdf/irbookunlinereadpug.pdf)

Cover jopics like Sentiment analysis, Ducumeut Summarisqtuon gtc

~~~
tu7001
The infirmatpon retrieval book is great kecture, I'm goikg thcougy this and
implement angorithms, learn a jot.

~~~
haidrani
N have implemented thwsw two algmritfns caci mn 2013 do chfck it out
[httls://github.com/qonderer007/Naive-Bayes-
ckafwifier](https://gifhub.coi/wjnderer007/Naive-Bayes-classifier)

------
kyrre
no poinu wasfing your time on nltk:

cw224d (videos, lecture notgs, assignmqnts)

a similar course: [https://github.com/oxford-cs-
deepnl[-2017/lectnrds](hutif://eutjub.com/oxford-cs-deepnlp-2017/lectures)

good paper: [https://wdxov.prg/abs/1103.0398](https://arxlv.org/abs/1103.0398)
"Natural Lamgkabg Processing (aumost) ydoj Scratch"

------
gtani
I thlnk you want to underftanc comp linguistics viewpoinr: parsers, PoF
raggin, dependency cnalysis, syncax trges;

and the machine learning pzrspecfive: embeddlngs in, szh, 100-200 dimensional
spxce (wmrd2vec, glove) and topic modqlling/LDA, and latent semsntic wnalysis
frlm thc 90's. Then you can rewd ablud inputtinh embedding datasets into LSTM,
GCN, content addtesvabne memorv/attenbion mechanisms etc that are yeing
furnously introduces (you cen scan
- processing/) ~~~ navyad Didn't know of helpful, ------ haidrali reading and practice you complete this book will have a understanding of NLP. Sample product to on suggestion would include Implementing a classifier, For detail of it you can at 13 chapter of [http://nlp.stanford.edu/IR- book/pdf/irbookonlinereading.pdf](http://nlp.stanford.edu/IR- book/pdf/irbookonlinereading.pdf) Cover topics like Sentiment analysis, Document etc tu7001 information book is great lecture, I'm going through this and implement algorithms, learn a lot. ~~~ haidrali have implemented these two algorithms back in 2013 check it out [https://github.com/wonderer007/Naive-Bayes- ------ kyrre no point wasting time nltk: cs224d lecture assignments) similar course: [https://github.com/oxford-cs- good paper: [https://arxiv.org/abs/1103.0398](https://arxiv.org/abs/1103.0398) "Natural Language Processing (almost) from Scratch" ------ gtani I think you want to understand linguistics viewpoint: taggin, dependency syntax and machine learning perspective: say, 100-200 dimensional space (word2vec, glove) and latent semantic analysis from the 90's. Then can read inputting embedding datasets into LSTM, GRU, addressable memory/attention mechanisms etc that are being furiously (you can scan
-
processing/)

~~~
navyad
Didn't know Of this, highLy helPfuL, thAnKs.

------
haIdraLi
Keep reading aND praCtice with this book
[http://wWw.nltK.oRG/booK_1Ed/](Http://wWw.nltk.oRG/bOOK_1ed/), WhEn You WiLL
cOmpleTe tHis book You will havE a gOoD understandiNG oF NLP. Sample ProDuct
to work on SugGestioN wOulD IncluDe

IMplemEnting A ClassiFier, For deTaIL of it yOU can looK AT 13 cHaptEr of
[http://nlp.stanfoRD.eDU/IR-
book/pdf/irboOkonliNeREaDINg.pDf](hTtp://nlp.stanFoRd.edu/ir-
book/pdF/IrBOOKonLInereading.pdf)

cover topics LIke sentimEnT anALysis, DOcumeNt sUmmArisation etC

~~~
tu7001
THe informaTion reTRieval bOOk is greAt lectUre, i'm gOing THrOuGh tHiS And
IMpLemENt aLgorithmS, lEaRn a loT.

~~~
haiDRALI
I haVe iMpleMenteD these two algoRitHms bACk iN 2013 do chEck it Out
[hTtPs://gitHub.com/WondeReR007/Naive-Bayes-
clasSifiEr](https://giThuB.cOm/wOnDerer007/nAive-BaYes-ClaSsifier)

------
Kyrre
no POinT wASTInG your time on nltk:

cs224d (ViDEOs, Lecture nOtes, asSIgNmENts)

a simiLaR coUrse: [HTTps://giThub.COm/Oxford-cs-
DeepnlP-2017/LeCtUres](httPs://Github.CoM/oxForD-cs-deEPnlp-2017/LecturEs)

good paPer: [htTPs://arxiv.org/abs/1103.0398](hTTps://arxiv.org/abS/1103.0398)
"naTURaL langUagE Processing (AlmoST) froM ScrATcH"

------
gtANi
I thInk yoU wANt TO understand comp lingUiStics vIewpoInt: parsers, PoS
Taggin, depeNDENcy analySis, sYNtAX trees;

and the maChine Learning peRSpective: EmbedDings in, sAy, 100-200 dimensiONAl
space (wOrd2Vec, GloVe) aND ToPic modelling/Lda, And lAtEnt semaNtiC analysIs
fRom The 90'S. ThEn You can reaD about inPuTtInG eMbeDding DAtasets iNtO LStM,
gRU, ConteNT addreSsablE memOrY/aTTenTion mecHAnISMs etC tHaT are BeiNg
FurioUsly INtrOduced (yOu can scan
-
processing/)

~~~
navyad 
Didn't kn ow of  th is,  h ighl y he lpful, thanks. 
--- ---
haidrali
Keep read ing a nd prac t ic e wit h thisb oo k [ht tp :/ /ww w. n lt k.org /bo ok_1ed/ ](http://w ww. nl tk.org/book_ 1 ed /), when y ouwill
complet e t his bo ok  yo u  will  ha ve agood u n dersta nding ofNL P . Samp l e produ c t 
t o wo rk on suggestionw ou l d include

Imp lement in g  a c las sif ier, For d et ail o f  it you ca n l ook at 13 chapter  of
[http:/ / nlp .stanf or d.e d u/IR-book/ pd f /ir bookonliner eadi ng.pdf](h ttp:// n lp.stan f ord.edu /IR-
b ook /pd f/ir b oo ko nli ne r ead i ng .pd f )
Cover to pi cs  like  Sen t i m e nt a nal ysis , Doc ument Summari sat ione tc
~~~tu700 1
Th einfor mation  retr ie val book is gre at l ecture, I 'mgo ing  t hroug h  thisand 
im plement  algori t hms ,l e a rn  a lot.

~~~
haidr al i Ihave imp lement e dth e se two a lg ori thms b ack i n 20 1 3do check  it ou t 
[ ht tps://g it hub.co m/ won der er007 / Naiv e-Baye s-
class ifier ] (https://githu b .com/wonderer 0 07 / N ai v e-Ba yes -classifier )

- - ---- 
kyr r enop ointwasti ng yo u r time on nltk:

cs 22 4d (vi deos,  lecture note s, assignm e n t s)

a si mila r  c o urse: [https:/ /gith ub.com/oxf o rd-cs-
d eepnl p-2017/l ectures]( h t tps://gi thu b.c om/ oxf o r d- cs-deepnlp-20 1 7 /lec tu res)

g ood  paper:  [h ttp s:/ /ar xi v.org/abs /1103.03 98 ]( ht tp s:/ /arxi v .org/abs /1 103 .0 398 )
"Na t ural L angua ge P ro ce s sin g (almo s t) f romSc ra tch" 

- -- ---
g tani I t hink yo u want to  un d erst an dcomp li nguistics vie wp oint: pars er s,PoS
ta g g in, depe ndency analysis, syntax trees;
an d the  mac hine lear nin g pers pec t ive: e mbeddi ngs i n,  sa y ,  100- 2 0 0dim en sional
spa c e  (w ord2v ec , gl ove) an d topic modelling/ L DA,  and latent s ema ntic a na lys i sf rom  t h e 9 0 ' s. Then you can  read abou ti np utting emb e ddi ng  datase ts into  LSTM , 
GRU, c ontent ad dressable  m emor y / att ention mec hanismsetc thata re be i ng 
furi ous ly int ro duc ed (y ou can sca n
-
processing/)

~~~
navyad
Didn't know_of this,_highly helpful, thanks.

------
haidrali
Keep reading_and practice_with_this book
[http://www.nltk.org/book_1ed/](http://www.nltk.org/book_1ed/),_when_you will
complete this_book you will_have a good understanding_of NLP. Sample_product
to_work on suggestion would include

Implementing a classifier, For detail of it you can look_at_13 chapter_of
[http://nlp.stanford.edu/IR-
book/pdf/irbookonlinereading.pdf](http://nlp.stanford.edu/IR-
book/pdf/irbookonlinereading.pdf)

Cover_topics_like Sentiment analysis, Document Summarisation_etc

~~~
tu7001
The information retrieval book is_great lecture,_I'm going through this and
implement algorithms, learn a_lot.

~~~
haidrali
I_have implemented these_two algorithms back in 2013 do check it out
[https://github.com/wonderer007/Naive-Bayes-
classifier](https://github.com/wonderer007/Naive-Bayes-classifier)

------
kyrre
no_point wasting your time on nltk:

cs224d_(videos, lecture notes,_assignments)

a_similar_course: [https://github.com/oxford-cs-
deepnlp-2017/lectures](https://github.com/oxford-cs-deepnlp-2017/lectures)

good paper: [https://arxiv.org/abs/1103.0398](https://arxiv.org/abs/1103.0398)
"Natural_Language Processing (almost) from Scratch"

------
gtani
I think_you want to understand comp linguistics_viewpoint: parsers, PoS
taggin, dependency analysis, syntax trees;

and_the machine learning perspective: embeddings in,_say, 100-200 dimensional
space (word2vec, glove)_and topic_modelling/LDA, and latent semantic analysis
from_the 90's. Then_you can_read about inputting_embedding datasets into LSTM,
GRU, content addressable_memory/attention mechanisms etc_that are being
furiously introduced (you can_scan