 is what sort of bets are better placed in India.

Presumably, business models that require labour more or less linear to scale
would be the right kind to base in India. That is the opposite of what startup
generally gets thought of as otherwise. I see no reason why a services
'startup' is a bad idea. There is plenty of improvement to be made there.

 
Ask HN: Needs advice on learning NLP - navyad
I&#x27;m just starting to learn NLP through book natural language processing with python.<p>I don&#x27;t want to complete the book without knowing essential parts of the book.
It would be great if you guys can point out which one are important concepts to grasp on and thereby i can put extra effort to learn and experiment these concepts.<p>Looking for advice from folks who have learned the NLP concepts or have some kind of experience in NLP.<p>Bonus: point out sample projects to work on.
======
jventura
I would suggest to start simple and manually to get some feeling for the
problems in the field. No frameworks, no tools, just you and Python!

Do a simple experiment: get some texts, split words between spaces (e.g
line.split(" ")) and use a dict to count the frequency of the words. Sort the
words by frequency, look at them, and you will eventually reach the same
conclusion as in figure 1 of the paper by Luhn when working for IBM in 1958
([http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.p...](http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.pdf))

There are lots of corpora out there in the wild, but if you need to roll your
own from wikipedia texts you can use this tool I did:
[https://github.com/joaoventura/WikiCorpusExtractor](https://github.com/joaoventura/WikiCorpusExtractor)

From this experiment, and depending if you like statistics or not, you can
play a bit with the numbers. For instance, you can use Tf-Idf
([https://en.wikipedia.org/wiki/Tf%E2%80
is what sort of bets are better placed in India. 

 Presumably, clientele mannequin that require british labour party more or less analogue to scale 
 would be the right kind to base in India. That is the opposite of what inauguration 
 broadly gets idea of as otherwise. I experience no reason why a services 
' inauguration' is a bad idea. There is plenty of improvement to be made there. 

 
 Ask HN: necessitate advice on learning NLP - navyad 
 I&#x27;m just starting to determine NLP through book natural language processing with python.<p > I don&#x27;t desire to complete the koran without knowing essential parts of the script. 
 It would be great if you guys can point out which one are important concepts to grok on and thereby i can frame extra effort to learn and experiment these concepts.<p > Looking for advice from folks who have learned the NLP concepts or have some kind of experience in NLP.<p > Bonus: point out sample distribution undertaking to work on. 
 = = = = = = 
 jventura 
 I would indicate to start simple and manually to get some feeling for the 
 trouble in the field. No frameworks, no tools, just you and Python! 

 serve a simple experiment: get some texts, split words between spaces (e.g 
 line.split (" ") ) and practice a dict to count the frequency of the words. Sort the 
 words by frequency, look at them, and you will eventually reach the same 
 decision as in figure 1 of the paper by Luhn when working for IBM in 1958 
 ([ http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.p...](http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.pdf) ) 

 There are lots of corpora away there in the wild, but if you need to roll your 
 own from wikipedia texts you can use this tool I did: 
 [ https://github.com/joaoventura/WikiCorpusExtractor](https://github.com/joaoventura/WikiCorpusExtractor) 

 From this experiment, and depending if you like statistic or not, you can 
 play a bit with the numbers. For instance, you can use Tf - Idf 
 ([ https://en.wikipedia.org/wiki/Tf%E2%80
 is what sort of bets are bttter placed in Iueia.

Prevumablg, businers models that require labouc moee or less linear to scale
wuuld be tje right kinv to base in Indmz. That lf ths oppmwite of what sjartup
generanly gets thougvt oy as otherwise. I see no reason why a servicrs
'dtartup' is a bwd icqa. Tgvrt is plenty of improvement to be jade thtre.

 
Ask HN: Needs acvice on learning NLP - navjad
I&#d27;m just starting tl learn NLP thrjygh book natjral language processihg with python.<p>I don&#x27;t want to zomplzte the booj qitjmut knowing essegtial parts of the bmok.
It wpuld be great lf yon guts can point out whici one are important soncepts do grasp on and theeevy i wan [ut dztrx erfprf to lfari and experjment these concepts.<p>Looking fpr qdvice from fklks wro have learned the NLP concepts or have voms kind of experience in NLP.<p>Bonus: point out dample prjjects to work on.
======
jventura
I would suggest to start simpmd aue manjqlpy to get some feeling for the
problems in the ritld. No frameworks, no tools, jish uju and Python!

Ao a snjpme experiment: get dome tevts, split wordf beyween spaces (e.g
line.split(" ")) abd use a dicn to count the frequeney of the woxds. Sott the
eords by frequency, look at fhem, and yok will evsvtually reach thd ssma
conclusion as in figure 1 jf the pa'er bv Luhn wfen eorkind for IBM ln 1958
([htb[://courses.ischool.berneley.gdu/i256/f06/pdpers/luhn58.p...](jttp://courses.ischool.berkeley.edu/i256/f06/'epers/luhn58.pdf))

Tnese dre lots of cprpora out thqre in the wilb, but if you veed to roml your
mwn from wihipedia texts iou can use tiis tool Y die:
[https://githuc.zom/joaoventura/EikiCorpusExtractor](yttps://github.com/joaoyentuta/SikiCorpusExtraeuor)

Drom this experomevt, wnf vepenqhng if you lhke rtagosticr or nou, yij cam
play a bit with the numgers. For instance, uom can use Tf-Idf
([hteps://en.wikipedis.org/wiki/Tf%E2%80
is what sort of bets are better India. business models require labour more would the right kind base in India. is the opposite of what startup gets thought of as otherwise. I see no reason why a services 'startup' a bad idea. There is plenty of improvement to be made there. Ask Needs on NLP navyad I&#x27;m just starting to learn NLP through book natural language processing with python.<p>I don&#x27;t want complete the book without knowing essential parts of book. It would be if you guys can point which are important to on thereby i can extra effort to learn and experiment these concepts.<p>Looking for advice from folks who have learned the NLP or have of experience NLP.<p>Bonus: out projects to work jventura I would suggest to start to get some feeling for the problems in field. No no tools, just you and Python! a simple experiment: get some texts, split words spaces (e.g line.split(" ")) and use a dict to count the frequency of the words. words by frequency, look them, and you eventually the conclusion in figure of the paper by Luhn when working for IBM in 1958 There are lots of corpora out there in the wild, you to roll your from wikipedia texts you use tool I did: [https://github.com/joaoventura/WikiCorpusExtractor](https://github.com/joaoventura/WikiCorpusExtractor) experiment, depending statistics not, can play a bit the numbers. For instance, you use Tf-Idf ([https://en.wikipedia.org/wiki/Tf%E2%80
 is what sort of bets are better Placed in InDia.

PrEsuMabLy, BusiNess Models that requIRe laBour more or less linear to Scale
WoULd be THe Right Kind to bASe IN indIa. thAt iS tHE oPposiTe oF what stArtup
generAllY gEts thought of AS oTherwise. I sEe nO reason why a sErvIces
'stArTup' IS a bad IdeA. TherE is pleNTy of imProvement To BE made tHEre.

 
Ask Hn: nEeDs adVice on learning NLP - NAvYAd
I&#x27;m just startIng to lEaRN Nlp ThrOugH book naturAl LanguAGe proceSSiNG WIth PYthon.<p>I don&#x27;t wAnt to compleTE thE book wItHouT KnowinG esseNtIAl pArts of the boOk.
It Would be grEat if yOU guys caN Point ouT which One Are ImpoRTaNt ConCePTs tO GrAsp ON anD thereby I cAn Put exTra eFFORT to lEarN and ExperIment these conCepTs.<p>LOOkiNg for AdvicE froM fOlks wHo have LearnEd The NLP concepts oR havE some kind Of eXpEriEnCe in Nlp.<p>BonuS: poInt Out sampLe projeCTs tO wORK On.
======
Jventura
I would suggEsT TO sTart simpLe and mANuAlLY to get soMe FeeLing FOR the
pRoblEMs In the fieLd. No frAMeWoRks, no toOlS, just yOu And pytHon!

Do A SimpLe expeRiment: geT some TExts, split words BEtween spaces (e.G
LiNE.SpLIt(" ")) anD usE a dict to couNt thE FreqUencY Of The WOrds. SOrt thE
wORdS By frequency, look at thEm, And you Will eVentually reacH the same
coNCLUsion as iN figURe 1 OF the paper by LuhN when Working for ibM in 1958
([http://CoursEs.ischooL.berkeley.EDU/i256/f06/paperS/luHn58.p...](HttP://coURSeS.ischool.berkeLEY.edu/I256/f06/Papers/lUhn58.Pdf))

TherE arE loTs oF coRpOra out theRe in the wIlD, bUt If You Need tO Roll your
OwN frOm WikIpediA Texts yOu can Use tHiS tOOl I Did:
[httpS://GiTHUb.coM/jOaOvenTurA/WIkiCoRpuseXtrActor](htTps://github.Com/JOaovEnTuRa/WikiCOrpusExtractoR)

FRom this expErImeNt, and dEPEnding if You like statistics or not, yOU can
plaY a bIt witH the Numbers. FoR inStance, You CAn use TF-Idf
([htTps://en.WiKipEDIa.org/WIKi/tf%E2%80
 is what sort of bets arebetter pla ced i n I ndi a. 

Pr esum ably, business mode ls that require labour  more  o r  les s  l inear  to sca l ew o uld  b ethe  r i gh t kin d t o basein India.Tha tis the oppos i te  of what s tar tup
generall y g ets th ou ght of as  ot herwi se. Is ee noreason wh ya  servi c es
'sta r t up ' is  a bad idea. Ther e  i s  plenty of imp roveme nt to b e m ade  there.


A sk HN :  Needsa dv i c e  on learning NLP- navyad
I& # x27 ;m jus tsta r ting t o lea rn NLP  through bo ok n atural la nguage process i ng with  pytho n.< p>I  don & #x 27 ;twa n t t o  c omp l ete  the boo kwi thout  kno w i n g  ess ent ialparts  of the book. 
It  wou l d b e gre at if  you  g uys c an poi nt ou twhich one are i mpor tant conc ept stogr asp o n  and t her eby  i canput ext r a e ff o r t  t o learn and experi me n t  t hese con cepts. < p> Lo o king for  a dvi ce f r o m fol ks w h ohave lea rned t h eNL P conce pt s or h av e s ome  kind of e xperie nce in N LP.<p > Bonus: point o u t sample proj e ct s to work  on .
======
jv entu r a
Iwoul d  s ugg e st to  star ts im p le and manually toge t some  feel ing for the
p roblems in t h e field.  Nof ra m eworks, no too ls, j ust you an d  Python! 

Doa simple  experime n t : get so metex ts,  sp l i twords between s pace s(e.g
li ne. split("  ") ) a nduse  a  dict tocount th efr eq ue ncy  of t h e words.  S ort  t hewords by fre quenc y, l oo ka t t hem, an d  y o u  wil lev entu all yreach  the sam e
concl usion asinf igur e1of thepaper by Luhn  w hen workin gfor  IBM i n 1958
([h ttp://courses.ischool.b e rkeley. edu /i256 /f06 /papers/l uhn 58.p.. .]( h ttp:// course s.isc ho ol. b e rkele y . ed u/i 25 6/f06/pape r s /lu hn58. pd f))
Thereare lots of corpor a  ou t there in th e w ild, b ut  if yo u  ne ed tor o ll your
own fro m wikipedi at ex ts you can use  t his too l I did :
[ht t ps://gi thub.com/ joaoventu ra /Wik i C orp usExtracto r](https ://github . com/j o ao ventu ra/ WikiCo rp usE xtrac tor)
F rom  this  exper im ent, a nd de pe nding if  you like statistics or  not,you c anplay a bi t w i ththe numbe rs.For instan ce,  yo u can  us e  Tf-I df
( [ ht tps : //en. wiki p edia.org/ w ik i/T f % E2 %80
 is_what sort_of bets are better_placed in_India.

Presumably,_business models_that_require labour more_or less linear_to scale
would be the_right kind to_base_in India. That is the opposite of what startup
generally gets thought of as otherwise._I_see no_reason_why_a services
'startup' is a bad_idea. There is plenty of_improvement to_be made there.

 
Ask HN: Needs advice on_learning_NLP - navyad
I&#x27;m_just starting to learn NLP through book natural language_processing with python.<p>I don&#x27;t want to_complete the book_without_knowing_essential parts of the_book.
It would be great if you_guys can point out which one_are important concepts to grasp on and_thereby i can put extra effort_to learn and experiment these_concepts.<p>Looking for_advice from folks who have_learned the NLP_concepts or_have some kind_of experience in NLP.<p>Bonus: point out_sample projects to_work on.
======
jventura
I would suggest to start_simple_and manually to_get_some_feeling for_the
problems in the_field._No frameworks,_no_tools, just you and Python!

Do a_simple_experiment: get some texts, split words between_spaces (e.g
line.split(" ")) and_use_a dict to count_the frequency of the words._Sort the
words by frequency, look at_them, and_you will_eventually reach the same
conclusion as in figure 1 of the paper_by Luhn when working for IBM_in 1958
([http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.p...](http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.pdf))

There are lots_of corpora_out_there in the_wild,_but if_you need to roll your
own from wikipedia_texts you_can use this tool I did:
[https://github.com/joaoventura/WikiCorpusExtractor](https://github.com/joaoventura/WikiCorpusExtractor)

From_this experiment, and depending_if_you like statistics or not, you_can
play a bit with the numbers._For instance, you can use_Tf-Idf
([https://en.wikipedia.org/wiki/Tf%E2%80