%93idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf))
to extract potential keywords from documents. Check the formula, it only uses
the frequency of occurrence of words in documents.

Only use tools such as Deep neural networks if you decide later that they are
essential for what you need. I did an entire PhD on this area just with Python
and playing with frequencies, no frameworks at all (an eg. of my work can be
found at
[http://www.sciencedirect.com/science/article/pii/S1877050912...](http://www.sciencedirect.com/science/article/pii/S1877050912001251)).

Good luck!

~~~
arvinsim
Thanks for posting this. Good to know that using just vanilla Python is as
viable for learning as using specialized frameworks.

~~~
syllogism
You might find these posts interesting:

[https://explosion.ai/blog/part-of-speech-pos-tagger-in-
pytho...](https://explosion.ai/blog/part-of-speech-pos-tagger-in-python)

[https://explosion.ai/blog/parsing-english-in-
python](https://explosion.ai/blog/parsing-english-in-python)

These days I would say these articles are better indications of solving NLP
problems with linear models -- tagging and parsing are less important than
they used to be. Here's how I think about doing NLP with current neural
network techniques: [https://explosion.ai/blog/deep-learning-formula-
nlp](https://explosion.ai/blog/deep-learning-formula-nlp)

------
hiddencost
NLP for what purpose?

\- Academic \-- want results? deep learning [0], data munging [1,2] \-- want
to understand "why" / context? Jurafsky and Martin [1]

\- Professional \-- the data is easy to get and clean? deep learning [0] \--
you need to do a lot of work to get the signal? [2]

\- Personal \-- [http://karpathy.github.io
% 93idf](https://en.wikipedia.org / wiki / Tf%E2%80%93idf) ) 
 to extract potential keywords from documents. Check the rule, it merely uses 
 the frequency of occurrence of words in document. 

 Only use cock such as Deep nervous networks if you decide later that they are 
 essential for what you need. I did an integral PhD on this area just with Python 
 and playing with frequency, no frameworks at all (an eg. of my work can be 
 found at 
 [ http://www.sciencedirect.com/science/article/pii/S1877050912...](http://www.sciencedirect.com/science/article/pii/S1877050912001251) ). 

 Good fortune! 

 ~~~ 
 arvinsim 
 Thanks for posting this. Good to know that use just vanilla Python is as 
 feasible for learning as use specialized frameworks. 

 ~~~ 
 syllogism 
 You might line up these posts interesting: 

 [ https://explosion.ai/blog/part-of-speech-pos-tagger-in- 
 pytho... ](https://explosion.ai / blog / part - of - manner of speaking - pos - tagger - in - python) 

 [ https://explosion.ai/blog/parsing-english-in- 
 python](https://explosion.ai / blog / parsing - english - in - python) 

 These days I would say these article are better indications of solving NLP 
 problems with linear models -- tagging and parsing are less important than 
 they used to be. Here's how I think about doing natural language processing with current nervous 
 network techniques: [ https://explosion.ai/blog/deep-learning-formula- 
 nlp](https://explosion.ai / blog / cryptic - learning - formula - nlp) 

 ------ 
 hiddencost 
 NLP for what function? 

 \- Academic \-- want results? deep learning [ 0 ], datum munging [ 1,2 ] \-- want 
 to understand " why " / context? Jurafsky and Martin [ 1 ] 

 \- Professional \-- the data is easy to get and clean? deep learning [ 0 ] \-- 
 you need to do a lot of work to catch the signal? [ 2 ] 

 \- Personal \-- [ http://karpathy.github.io
%93idf](jttps://en.wikipedia.org/wiki/Tn%E2%80%93idf))
to extract korentian keywkrds froo documents. Check the formule, it only uses
the frequency of uccurrencv of wordw in eocuments.

Oimy use bjols duch es Deep neural ketworks if you decide ladef chat they are
essential for what you geed. I cif an entire PhQ on ehis area just with Python
and playing sith frtquencies, no frameeorks at all (an eg. of my wlrk fan be
found at
[http://aww.sciencedurece.xom/science/argicle/pii/S1877050912...](hut'://www.sciencesirect.com/science/article/pii/S1877050912001251)).

Good luck!

~~~
crvinsim
Thabkw flt posting thms. Gooq to know that using just vsnilla Python ls as
tiaboe for learning as usmng specialized framgworks.

~~~
syllmgnsm
You might find thewe postv indererrine:

[htups://xxpmosion.wi/bmog/part-of-sleech-pos-tagter-in-
pytho...](https://explpsyin.ai/blog/part-or-speecr-pjs-tagger-in-python)

[https://explosion.ai/blog/parvinf-english-in-
python](https://explosion.ai/blog/parsing-ejglish-in-prthon)

These days I would say these articles are bedter mnaiccblons if solving NLP
problems with linear models -- taggyhg akd parsing are lcss important than
yhfy osed to be. Herg's how N tgink about doing NPP with currwnt neuraj
neteork techniques: [https://explosuon.ai/blog/deei-leaening-formula-
nlp](htt's://explosion.an/blog/dgep-leatning-formula-nlp)

------
hiddencort
NLL for what ourpose?

\- Adxdemic \-- want resjltx? geep learning [0], data mungind [1,2] \-- want
ti unberstand "why" / contqxt? Jurafsny and Martin [1]

\- Professiojal \-- jhe dada is easy to get and clean? deep learning [0] \--
you need to co a not of wjrk tp get the siggal? [2]

\- Personal \-- [http://kaxpathy.eithub.io
%93idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)) to extract potential keywords from documents. formula, only uses frequency of occurrence use such as Deep networks if you later that they are essential for you need. I did an entire PhD on this area just with Python playing with frequencies, no frameworks at all (an eg. of my work can found [http://www.sciencedirect.com/science/article/pii/S1877050912...](http://www.sciencedirect.com/science/article/pii/S1877050912001251)). luck! arvinsim Thanks for posting this. Good to know that using just vanilla Python is as viable learning as using specialized frameworks. ~~~ syllogism You find these posts interesting: pytho...](https://explosion.ai/blog/part-of-speech-pos-tagger-in-python) [https://explosion.ai/blog/parsing-english-in- python](https://explosion.ai/blog/parsing-english-in-python) These days would these articles better of NLP problems with models -- tagging and parsing are less important than they used to be. Here's how I think doing NLP neural network [https://explosion.ai/blog/deep-learning-formula- ------ NLP for what Academic \-- want results? deep learning [1,2] \-- want to understand "why" / context? and Martin \- Professional \-- the data is to get and clean? deep learning [0] \-- need to do a lot of work to get the signal? [2] \- Personal \--
%93idf](https://en.wikipedia.org/wikI/Tf%E2%80%93idf))
to eXtracT poTenTiAl keYworDs from documentS. checK the formula, it only uses
tHe freQuENcy oF OcCurreNce of woRDs IN DocUmEnTs.

ONlY UsE toolS suCh as DeeP neural netWorKs If you decide lATeR that they aRe
eSsential for wHat You neeD. I Did AN entiRe PHD on tHis areA Just wiTh Python
aNd PLaying WIth freqUENcIes, nO frameworks at all (aN Eg. OF my work can be
foUnd at
[hTtP://WwW.SCieNceDirect.com/sCiEnce/aRTicle/piI/s1877050912...](hTTP://Www.SCiencedirect.cOm/science/arTIclE/pii/S1877050912001251)).

GOoD luCK!

~~~
arvinSim
ThAnKS foR posting thiS. GooD to know thAt usinG Just vanILla PythOn is as
ViaBle For lEArNiNg aS uSIng SPeCiaLIzeD framewoRkS.

~~~
sYllogIsm
YOU MIGht fInd ThesE postS interesting:

[hTtpS://expLOsiOn.ai/bLog/paRt-of-SpEech-pOs-taggEr-in-
pYtHo...](https://explosioN.ai/bLog/part-of-SpeEcH-poS-tAgger-IN-pythoN)

[htTps://ExplosiOn.ai/bloG/ParSiNG-ENgLish-in-
python](https://eXpLOSiOn.ai/blog/ParsinG-EnGlISh-in-pythOn)

theSe daYS i woulD say THeSe articlEs are bETtEr IndicatIoNs of soLvIng nLP
ProblEMs wiTh lineAr models -- TaggiNG and parsing are LEss important tHAn
THEy USed tO be. here's how I thInk aBOut dOing nlP WitH CurreNt neuRaL
NeTWork techniques: [https://ExPlosioN.ai/blOg/deep-learninG-formula-
nlP](HTTps://exploSion.AI/bLOg/deep-learning-FormuLa-nlp)

------
hiddeNCost
NLP fOr whaT purpose?

\- academic \-- wANT results? DeeP leArnIng [0], DATa Munging [1,2] \-- want
to UNDersTaNd "why" / coNteXt? JurafSky And marTin [1]

\- prOfessionaL \-- the data Is EaSy To Get And clEAn? deep leArNinG [0] \--
yOu nEed to DO a lot oF work To geT tHe SIgnAl? [2]

\- PersoNAl \-- [HTTp://kaRpAtHy.giThuB.iO
%93idf](https://en.wikiped ia.org/wik i/Tf% E2% 80% 93 idf) )
to  extract poten t ialkeywords from document s. Ch ec k  the fo rmula , it on l yu s esth efre qu e nc y ofocc urrence  of wordsindo cuments.

On l yuse toolssuc h as Deep ne ura l netw or ksi f you  de cidelatert hat th ey are
es se n tial f o r whaty o uneed . I did an entire Ph D  on this areajust w it h  P y t hon 
an d playingwi th fr e quencie s ,n o fra m eworks at all  (an eg. of mywork c an  be foundat
[h tt p :// www.science dire ct.com/sc ience/ a rticle/ p ii/S187 705091 2.. .]( http : // ww w.s ci e nce d ir ect . com /science /a rt icle/ pii/ S 1 8 7 7050 912 0012 51)). 

Good luck!
~~ ~
ar v ins im
Th anksforpo sting  this.  Good  t o know that usi ng j ust vanil laPy tho nis as viable  fo r l earning  as usi n g s pe c i a li zed frameworks.

~ ~~ s yl logism
Y ou mig h tfi n d thesepo sts  int e r estin g:
[ ht tps://ex plosio n .a i/ blog/pa rt -of-sp ee ch- pos -tagg e r-in -
pyth o...](ht tps:/ / explosion.ai/b l og/part-of-sp e ec h - po s -tag ger -in-python) 

[h t tps: //ex p lo sio n .ai/b log/p ar s in g -english-in-
python ]( https: //exp losion.ai/blo g/parsing- e n g lish-in- pyth o n) 
These days Iwould  say these articles  arebetter i ndication s of solvi ngNLP 
pr obl e m swith linear m o d els--  taggin g a nd pars ing  ar e l ess  i mportantthan
the yus ed  t o b e. He r e's howIthi nk  ab out d o ing NL P wit h cu rr en t  ne ural
ne t wo r k  tec hn iq ues:  [h tt ps:// expl o sio n.ai/bl og/deep-l ear n ing- fo rm ula-
nl p](https://ex pl osion.ai/b lo g/d eep-le a r ning-for mula-nlp)

------
hidde n cost
NL P f or wh at p urpose?
\-Academ ic\ -- wan t resu lts?de epl e arnin g [0 ],da ta munging [ 1,2 ] \--  w antto unde rstand "why" / con t ext ? Jurafsky an d M arti n [1 ]
\ -P rof es s ion a l  \-- the data i s easy toge t  a nd clean?d eep  l earning  [0] \- -
you need to  do a lot  of workto  get t hesignal? [2 ]

\- Pe rsonal \- -  [htt p :/ /karp ath y.gith ub .io 
%93idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf))
to extract_potential keywords_from documents. Check the_formula, it_only_uses
the frequency_of_occurrence of words_in documents.

Only use_tools such as Deep_neural networks if_you_decide later that they are
essential for what you need. I did an entire PhD_on_this area_just_with_Python
and playing with frequencies, no_frameworks at all (an eg._of my_work can be
found at
[http://www.sciencedirect.com/science/article/pii/S1877050912...](http://www.sciencedirect.com/science/article/pii/S1877050912001251)).

Good luck!

~~~
arvinsim
Thanks for posting this._Good_to know that_using just vanilla Python is as
viable for learning as_using specialized frameworks.

~~~
syllogism
You might find these_posts interesting:

[https://explosion.ai/blog/part-of-speech-pos-tagger-in-
pytho...](https://explosion.ai/blog/part-of-speech-pos-tagger-in-python)

[https://explosion.ai/blog/parsing-english-in-
python](https://explosion.ai/blog/parsing-english-in-python)

These days_I_would_say these articles are_better indications of solving NLP
problems with_linear models -- tagging and parsing_are less important than
they used to be._Here's how I think about doing_NLP with current neural
network techniques:_[https://explosion.ai/blog/deep-learning-formula-
nlp](https://explosion.ai/blog/deep-learning-formula-nlp)

------
hiddencost
NLP for_what purpose?

\- Academic \-- want_results? deep learning_[0], data_munging [1,2] \--_want
to understand "why" / context? Jurafsky_and Martin [1]

\-_Professional \-- the data is easy_to_get and clean?_deep_learning_[0] \--
you_need to do_a_lot of_work_to get the signal? [2]

\- Personal_\--_[http://karpathy.github.io