 of: IN of: IN his: PRP$ his: PRP$ property: NN property: NN from: IN from: IN respondent: NN respondent: NN St.: NNP St.: NNP Johns: NNP Johns: NNP River: NNP River: NNP Water: NNP Water: NNP Management: NNP Management: NNP District: NNP District: NNP (: NNP -LRB-: -LRB- District: NNP District: NNP ): NNP -RRB-: -RRB-,:,,:, which: WDT which: WDT,:,,:, consistent: VBD consistent: JJ with: IN with: IN Florida: NNP Florida: NNP law: NN law: NN,:,,:, requires: VBZ requires: VBZ permit: NN permit: NN applicants: NNS applicants: NNS wishing: VBG wishing: VBG to: TO to: TO build: VB build: VB on: IN on: IN wetlands: NNS wetlands: NNS to: TO to: TO offset: VB offset: VB the: DT the: DT resulting: VBG resulting: VBG environmental: JJ environmental: JJ damage: NN damage: NN.:.’.:.

Now, for a really interesting example: gibberish made to look like English. For this test, we’re going to use Lewis Carroll’s Jabberwocky:

nltk.pos_tag(nltk.word_tokenize(""" Twas bryllyg, and ye slythy toves Did gyre and gymble in ye wabe: All mimsy were ye borogoves; And ye mome raths outgrabe. """))

At last, we have something where the output varies. One obvious lesson from this is that these algorithms are more than happy to guess to improve accuracy, even where they have no idea what’s going on, a similar strategy to multiple choice tests. It may be prudent to develop a class of algorithms which lose points for consistently guessing wildly incorrectly (similar to the scoring method used on the SATs).

Twas: NNP Twas: NNP bryllyg: NN bryllyg: NN,:,,:, and: CC and: CC ye: VB ye: NN slythy: JJ slythy: NN toves
of: IN of: IN his: PRP$ his: PRP$ property: NN property: NN from: IN from: IN respondent: NN respondent: NN St.: NNP St.: NNP Johns: NNP Johns: NNP River: NNP River: NNP Water: NNP Water: NNP Management: NNP Management: NNP District: NNP District: NNP (: NNP -LRB-: -LRB- District: NNP District: NNP ): NNP -RRB-: -RRB-, :, ,: , which: WDT which: WDT, :, ,: , reproducible: VBD reproducible: JJ with: IN with: IN Florida: NNP Florida: NNP law: NN law: NN, :, ,: , requires: VBZ ask: VBZ permit: NN permit: NN applicant: NNS applicants: NNS wishing: VBG wishing: VBG to: TO to: TO construct: VB build: VB on: IN on: IN wetlands: NNS wetlands: NNS to: TO to: TO cancel: VB offset: VB the: DT the: DT resulting: VBG resulting: VBG environmental: JJ environmental: JJ damage: NN price: NN. :. ’. :. 

 Now, for a really interesting example: gibberish made to look like English. For this trial, we ’re going to use Lewis Carroll ’s Jabberwocky: 

 nltk.pos_tag(nltk.word_tokenize (" " " Twas bryllyg, and ye slythy toves Did coil and gymble in ye wabe: All mimsy were ye borogoves; And ye mome raths outgrabe. " " ") ) 

 At last, we have something where the output change. One obvious lesson from this is that these algorithm are more than happy to estimate to improve accuracy, even where they have no idea what ’s belong on, a similar strategy to multiple choice tests. It may be prudent to develop a course of algorithm which suffer points for consistently guessing wildly incorrectly (similar to the scoring method used on the SATs). 

 Twas: NNP Twas: NNP bryllyg: NN bryllyg: NN, :, ,: , and: milliliter and: milliliter ye: VB ye: NN slythy: JJ slythy: NN toves
 of: IN of: IN his: PRP$ his: PRK$ property: NN prokeety: NN from: JN from: KN respondent: NN respondent: IN Sr.: NNP St.: NNP Johns: NNP Johnr: NNP Rivvr: NNP Ricer: INP Water: NNP Wavsr: NNP Managejcnt: NUP Management: NNK District: NTP District: NN[ (: NUP -LRB-: -LRB- District: NNP District: NNP ): NNP -RTB-: -RRB-,:,,:, which: WDT whibh: WDT,:,,:, boksistent: VBD consistent: JJ with: JN with: IN Florida: NMP Florida: NNP law: NN law: JN,:,,:, rfquires: VBZ requirfs: VBZ permut: NG permit: NN aoplicants: NNS applicanjs: NNS wishing: VBG wishing: VBG tu: TO co: TO build: VV bkhld: VB on: II on: IG wetlands: NKX wetldnds: NNX to: TO to: TO pffvet: VB offset: VB the: DT vhe: DT resulting: VBG resultinc: RBG environmental: JJ wncironkentdl: JG daoagt: NI dzmage: JN.:.’.:.

Nkw, for a rsally interwsting example: gibbtrify made to looi like Egglish. For this test, we’re going to use Newjs Carroll’s Jabberwocky:

bltk.pos_tag(nltk.word_tokgnize(""" Twas bryllyg, and ye slythy toves Did gyre and gymble hn ye dabt: Qll mknsj were ye borogoves; And ye mome raths outgrabq. """))

Au lsst, we have soiething whete tng output varier. One obvjous lesson from tjis is jhat tyese algowithks are more than happy to gyess to imprjce accuracy, even wkere they hare no odea ehat’s going on, a similax strafegy to mulhiple chojze tests. It may ce irudant to dtxelop a class of wlgorithmw whnch lose poimts fow consistejtly ngessing wildly inclrrecjly (sikilar to tje scoring method used on the SEVs).

Twas: NNP Twss: NNI bryllyg: NN bvyllyg: NN,:,,:, and: CS and: CC ye: VB ye: NN flythh: JJ slythj: NN tovev
of: IN of: IN his: PRP$ his: NN NN from: from: IN respondent: St.: Johns: NNP Johns: River: NNP River: Water: NNP Water: NNP Management: NNP NNP District: NNP District: NNP (: NNP -LRB-: -LRB- District: NNP District: NNP NNP -RRB-: -RRB-,:,,:, which: WDT which: WDT,:,,:, consistent: VBD consistent: JJ with: IN IN NNP NNP NN law: NN,:,,:, requires: VBZ requires: VBZ permit: NN permit: NN applicants: NNS applicants: NNS wishing: wishing: VBG to: TO to: TO build: VB VB on: IN on: wetlands: NNS wetlands: NNS to: to: offset: VB VB DT DT resulting: VBG VBG environmental: JJ environmental: JJ damage: NN damage: NN.:.’.:. Now, for a really interesting example: gibberish made look like this test, going use Carroll’s Jabberwocky: nltk.pos_tag(nltk.word_tokenize(""" and ye slythy toves Did gyre ye wabe: All mimsy were ye borogoves; And mome raths """)) At last, we have something the output varies. One obvious lesson from this that these algorithms are more than happy to guess to improve accuracy, even where they idea what’s going on, similar strategy to choice It be to develop class of algorithms which lose points for consistently guessing wildly incorrectly to the scoring method used on the SATs). Twas: NNP bryllyg: bryllyg: NN,:,,:, and: and: CC ye: VB NN JJ slythy: NN toves
 of: IN of: IN his: PRP$ his: PRP$ propeRty: NN propeRty: NN FroM: IN FrOm: IN RespOndent: NN responDEnt: Nn St.: NNP St.: NNP Johns: NNP JohNs: NNP riVEr: NNp riVer: NNp Water: Nnp WATEr: NnP maNagEmENt: nNP MaNagEment: NNp District: NnP DIsTrict: NNP (: NNP -Lrb-: -LrB- District: nNP district: NNP ): NnP -RrB-: -RRB-,:,,:, wHiCh: Wdt whicH: WDt,:,,:, consIstent: vbD consIstent: JJ wItH: iN with: in FloridA: nnP florIda: NNP law: NN law: NN,:,,:, rEQuIRes: VBZ requires: vBZ perMiT: nN PERmiT: NN Applicants: nNs applICants: NNs WiSHINg: Vbg wishing: VBG to: tO to: TO build: vb buIld: VB oN: In on: in wetlaNds: NNs wETlaNds: NNS to: TO tO: TO oFfset: VB ofFset: VB THe: DT the: dt resultIng: VBG ResUltIng: Vbg eNvIroNmENtaL: jJ EnvIRonMental: JJ DaMaGe: NN dAmagE: nn.:.’.:.

nOw, foR a rEallY inteResting examplE: giBberISh mAde to Look lIke ENgLish. FOr this Test, wE’rE going to use LewiS CarRoll’s JabbErwOcKy:

nLtK.pos_tAG(nltk.wOrd_TokEnize(""" TwAs bryllYG, anD yE SLYtHy toves Did gyre and gYmBLE iN ye wabe: ALl mimsY WeRe YE borogovEs; and Ye moME Raths OutgRAbE. """))

At last, wE have sOMeThIng wherE tHe outpUt VarIes. one obVIous Lesson From this Is thaT These algorithmS Are more than haPPy TO GuESs to ImpRove accuracY, eveN WherE theY HaVe nO Idea wHat’s gOiNG oN, A similar strategy to mUlTiple cHoice Tests. It may be pRudent to deVELOp a class Of alGOrIThms which lose pOints For consistENtly guesSing wIldly incOrrectly (sIMIlar to thE scOriNg mEthOD UsEd on the SATs).

TwAS: nNP TWaS: NNP bryLlyG: NN brylLyg: nN,:,,:, aNd: Cc anD: Cc ye: VB ye: NN Slythy: JJ SlYtHy: nN TovEs
 of: IN of: IN his: PRP$ h is: PRP$ p roper ty:  NN  p rope rty:  NN from: IN f r om:IN respondent: NN resp onden t: NN S t .:  NNPSt.: NN P  J o h ns:  N NP  Jo hn s :NNP R ive r: NNPRiver: NNP  Wa te r: NNP Water :  N NP Managem ent : NNP Manage men t: NNP  D ist r ict:NNP  Dist rict:N NP (:NNP -LRB- :- LRB- D i strict: N NP  Dis trict: NNP ): NNP -R R B-: -RRB-,:,,: , whic h: WD T whi ch:  WDT,:,,:,  c onsis t ent: VB D  c o n s ist e nt: JJ with:IN with: IN Flo rida:NN P F l orida:  NNPla w : N N law: NN,: ,,:,  requires : VBZr equires :  VBZ pe rmit:NNper mit: NN  a ppl ic a nts :  N NSa ppl icants:NN Swishi ng:V B G wish ing : VB G to:  TO to: TO bu ild : VB bui ld: V B on:  INon : INwetlan ds: N NS  wetlands: NNSto:TO to: TO  of fs et:  V B off s et: VB  th e:DT the:  DT res u lti ng : V BG  resulting: VBG en vi r o nm ental: J J envi r on me n tal: JJda mag e: N N damag e: N N .: .’.:.

N ow, fo r  a  r eally i nt eresti ng  ex amp le: g i bber ish ma de to lo ok li k e English. For this test, we ’ re g oi n g to  us e Lewis Car roll ’ s Ja bber w oc ky: 
nltk .pos_ ta g (n l tk.word_tokenize("" "Twas b rylly g, and ye sly thy tovesD i d  gyre an d gy m bl e  in ye wabe: A ll mi msy were y e  borogov es; A nd ye mo me rathso u tgrabe.""" ))
At  la s t ,we have somet h i ng w he re theout put var ies . O neobv io us lesson  from th is  i sth atthese algorith ms  ar emor e tha n  happy  to g uess  t oi mpr ove acc u ra c y , ev en  w here  th ey  have  noi dea  what’s  going on , a simi la rstrateg y to multiple  c hoice test s.  It  may b e prudentto develop a class of a l gorithm s w hichlose  points f orconsis ten t ly gue ssingwildl yinc o r rectl y (s imi la r to the s c o rin g met ho d us ed on t he SATs).

Twas: N N P T was: NNP bryl lyg : NN b ry lly g :N N,: ,, : , a n d : CC and: CC ye : VB ye: N Ns ly thy: JJ sl y thy :NN tove s
 of:_IN of:_IN his: PRP$ his:_PRP$ property:_NN_property: NN_from:_IN from: IN_respondent: NN respondent:_NN St.: NNP St.:_NNP Johns: NNP_Johns:_NNP River: NNP River: NNP Water: NNP Water: NNP Management: NNP Management: NNP District:_NNP_District: NNP_(:_NNP_-LRB-: -LRB- District: NNP District:_NNP ): NNP -RRB-: -RRB-,:,,:,_which: WDT_which: WDT,:,,:, consistent: VBD consistent: JJ with: IN_with:_IN Florida: NNP_Florida: NNP law: NN law: NN,:,,:, requires: VBZ requires:_VBZ permit: NN permit: NN applicants:_NNS applicants: NNS_wishing:_VBG_wishing: VBG to: TO_to: TO build: VB build: VB_on: IN on: IN wetlands: NNS_wetlands: NNS to: TO to: TO offset:_VB offset: VB the: DT the:_DT resulting: VBG resulting: VBG_environmental: JJ_environmental: JJ damage: NN damage:_NN.:.’.:.

Now, for a_really interesting_example: gibberish made_to look like English. For this_test, we’re going_to use Lewis Carroll’s Jabberwocky:

nltk.pos_tag(nltk.word_tokenize(""" Twas_bryllyg,_and ye slythy_toves_Did_gyre and_gymble in ye_wabe:_All mimsy_were_ye borogoves; And ye mome raths_outgrabe._"""))

At last, we have something where the_output varies. One obvious_lesson_from this is that_these algorithms are more than_happy to guess to improve accuracy,_even where_they have_no idea what’s going on, a similar strategy to multiple choice_tests. It may be prudent to_develop a class of_algorithms which_lose_points for consistently_guessing_wildly incorrectly_(similar to the scoring method used on_the SATs).

Twas:_NNP Twas: NNP bryllyg: NN bryllyg:_NN,:,,:, and: CC and:_CC_ye: VB ye: NN slythy: JJ_slythy: NN toves