Unanswerable
NONE
No
BOOLEAN
We would like to be able to represent each car with the most relevant car-speak terms. We can do this by filtering each review using the NLTK library BIBREF8, only retaining the most relevant words. First we token-ize each review and then keep only the nouns and adjectives from each review since they are the most salient parts of speech BIBREF9. This leaves us with $10,867$ words across all reviews. Figure FIGREF6 shows the frequency of the top 20 words that remain. So far we have compiled the most relevant terms in from the reviews. We now need to weight these terms for each review, so that we know the car-speak terms are most associated with a car. Using TF-IDF (Term Frequency-Inverse Document Frequency) has been used as a reliable metric for finding the relevant terms in a document BIBREF10.
2002.02070
Introduction </s> A Ġlarge Ġportion Ġof Ġthe Ġcar - bu ying Ġexperience Ġin Ġthe ĠUnited ĠStates Ġinvolves Ġinteractions Ġat Ġa Ġcar Ġdealership ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠTrad itionally , Ġa Ġcar Ġdealer Ġlistens Ġand Ġunderstands Ġthe Ġneeds Ġof Ġthe Ġclient Ġand Ġhelps Ġthem Ġfind Ġwhat Ġcar Ġis Ġright Ġbased Ġon Ġtheir Ġneeds . </s> With Ġthe Ġadvent Ġof Ġthe Ġinternet , Ġmany Ġpotential Ġcar Ġbuyers Ġtake Ġto Ġthe Ġweb Ġto Ġresearch Ġcars Ġbefore Ġgoing Ġto Ġa Ġdealership Ġin Ġperson ĠB IB REF 0 , ĠB IB REF 2 . ĠHowever , Ġnearly Ġ50 % Ġof Ġcustomers Ġbought Ġa Ġcar Ġat Ġthe Ġdealership Ġbased Ġon Ġthe Ġsales Ġrepresentative 's Ġadvice , Ġnot Ġtheir Ġown Ġresearch ĠB IB REF 1 , ĠB IB REF 2 . </s> Throughout Ġthis Ġinteraction Ġthe Ġdealer Ġis Ġacting Ġas Ġa Ġtype Ġof Ġtranslator Ġor Ġclass ifier . ĠThe Ġdealer Ġtakes Ġa Ġnatural Ġlanguage Ġinput Ġ( e . g . ĠâĢ ľ I Ġneed Ġa Ġfast , Ġfamily Ġfriendly , Ġreliable Ġcar Ġunder Ġ$ 20 k âĢ Ŀ ) Ġand Ġreturns Ġa Ġlist Ġof Ġsuggestions . ĠThe Ġdealer Ġunderstands Ġthe Ġideas Ġof ĠâĢ ľ fast âĢ Ŀ , ĠâĢ ľ family Ġfriendly âĢ Ŀ , Ġand ĠâĢ ľ rel iable âĢ Ŀ Ġand Ġis Ġable Ġto Ġcome Ġup Ġwith Ġa Ġreasonable Ġrecommendation Ġbased Ġon Ġthis Ġknowledge . </s> In Ġthis Ġpaper Ġwe Ġaim Ġto Ġcreate Ġa Ġsystem Ġthat Ġcan Ġunderstand Ġcar - speak Ġbased Ġon Ġsome Ġnatural Ġlanguage Ġinput Ġ( we Ġwant Ġto Ġrecreate Ġthe Ġdealer Ġfrom Ġabove ). ĠBut Ġhow Ġdo Ġwe Ġprepare Ġa Ġproper Ġtraining Ġset Ġfor Ġa ĠNatural ĠLanguage Ġmodel ? ĠWhat Ġmodel Ġis Ġbest Ġsuited Ġto Ġthis Ġproblem ? ĠCan Ġthis Ġmodel Ġtake Ġa Ġhuman Ġout Ġof Ġthe Ġcar - bu ying Ġprocess ? ĠTo Ġanswer Ġthese Ġquestions , Ġthe Ġremainder Ġof Ġthis Ġpaper Ġmakes Ġthe Ġfollowing Ġcontributions : </s> Def ining ĠâĢ ľ car - speak âĢ Ŀ Ġand Ġits Ġrole Ġin Ġthe Ġcar - bu ying Ġprocess . </s> App ropri ate Ġtraining Ġdata Ġfor Ġa ĠNatural ĠLanguage Ġmodel . </s> A Ġmodel Ġthat Ġis Ġable Ġto Ġproperly Ġclassify Ġcar - speak Ġand Ġreturn Ġa Ġcar . </s> We Ġaim Ġto Ġaccomplish Ġthese Ġgoals Ġin Ġa Ġscientific Ġmanner , Ġusing Ġreal Ġdata Ġand Ġmodern Ġmethods . </s> Related ĠWork </s> There Ġhas Ġbeen Ġsome Ġwork Ġdone Ġin Ġthe Ġfield Ġof Ġcar - s ales Ġand Ġdealer Ġinteractions . ĠHowever , Ġthis Ġis Ġthe Ġfirst Ġwork Ġthat Ġspecifically Ġfocuses Ġon Ġthe </s> Del o itte Ġhas Ġpublished Ġa Ġreport Ġon Ġthe Ġentire Ġcar - bu ying Ġprocess ĠB IB REF 0 . ĠThe Ġreport Ġgoes Ġinto Ġgreat Ġdepth Ġabout Ġthe Ġmethods Ġpotential Ġbuyers Ġuse Ġto Ġfind Ġnew Ġcars Ġto Ġbuy , Ġand Ġhow Ġthey Ġgo Ġabout Ġbuying Ġthem . ĠThe Ġreport Ġtells Ġus Ġthat Ġthere Ġare Ġseveral Ġunique Ġphases Ġthat Ġa Ġpotential Ġbuyer Ġgoes Ġthrough Ġbefore Ġbuying Ġa Ġcar . </s> Ver ho ef Ġet Ġal . Ġlooked Ġat Ġthe Ġspecifics Ġof Ġdealer Ġinteraction Ġand Ġhow Ġdealers Ġretain Ġcustomers ĠB IB REF 3 . ĠVer ho ef Ġtells Ġus Ġhow Ġimportant Ġdealers Ġare Ġto Ġthe Ġcar - bu ying Ġprocess . ĠHe Ġalso Ġexplains Ġhow Ġinfluential Ġa Ġdealer Ġcan Ġbe Ġon Ġwhat Ġcar Ġthe Ġbuyer Ġpurchases . </s> Jeff ĠKers hner Ġcompiled Ġa Ġseries Ġof Ġstatistics Ġabout ĠDeal ership ĠSales ĠB IB REF 1 . ĠThese Ġstatistics Ġfocus Ġon Ġsmall Ġsocial Ġinteractions ĠB IB REF 4 Ġbetween Ġthe Ġdealer Ġand Ġthe Ġbuyer . </s> Bar ley Ġexplains Ġthe Ġincreasing Ġrole Ġof Ġtechnology Ġin Ġthe Ġcar - bu ying Ġprocess ĠB IB REF 2 . ĠBar ley Ġtells Ġus Ġthat Ġusers Ġprefer Ġto Ġuse Ġtechnology / rob ots Ġto Ġfind Ġthe Ġcars Ġthey Ġwant Ġto Ġbuy Ġinstead Ġof Ġgoing Ġto Ġa Ġdealer , Ġdue Ġthe Ġdistrust Ġtowards Ġsales Ġrepresentatives . </s> What Ġis ĠCar - speak ? </s> When Ġa Ġpotential Ġbuyer Ġbegins Ġto Ġidentify Ġtheir Ġnext Ġcar - p urchase Ġthey Ġbegin Ġwith Ġidentifying Ġtheir Ġneeds . ĠThese Ġneeds Ġoften Ġcome Ġin Ġthe Ġform Ġof Ġan Ġabstract Ġsituation , Ġfor Ġinstance , ĠâĢ ľ I Ġneed Ġa Ġcar Ġthat Ġgoes Ġreally Ġfast âĢ Ŀ . ĠThis Ġcould Ġmean Ġthat Ġthey Ġneed Ġa Ġcar Ġwith Ġa ĠV 8 Ġengine Ġtype Ġor Ġa Ġcar Ġthat Ġhas Ġ500 Ġhorsepower , Ġbut Ġthe Ġbuyer Ġdoes Ġnot Ġknow Ġthat , Ġall Ġthey Ġknow Ġis Ġthat Ġthey Ġneed Ġa ĠâĢ ľ fast âĢ Ŀ Ġcar . </s> The Ġterm ĠâĢ ľ fast âĢ Ŀ Ġis Ġcar - speak . ĠCar - speak Ġis Ġabstract Ġlanguage Ġthat Ġper tains Ġto Ġa Ġcar 's Ġphysical Ġattribute ( s ). ĠIn Ġthis Ġinstance Ġthe Ġphysical Ġattributes Ġthat Ġthe Ġterm ĠâĢ ľ fast âĢ Ŀ Ġper tains Ġto Ġcould Ġbe Ġthe Ġhorsepower , Ġor Ġit Ġcould Ġbe Ġthe Ġcar 's Ġform Ġfactor Ġ( how Ġthe Ġcar Ġlooks ). ĠHowever , Ġwe Ġdo Ġnot Ġknow Ġexactly Ġwhich Ġattributes Ġthe Ġterm ĠâĢ ľ fast âĢ Ŀ Ġrefers Ġto . </s> The Ġuse Ġof Ġcar - speak Ġis Ġpresent Ġthroughout Ġthe Ġcar - bu ying Ġprocess . ĠIt Ġbegins Ġin Ġthe ĠResearch ĠPhase Ġwhere Ġbuyers Ġidentify Ġtheir Ġneeds ĠB IB REF 0 . ĠWhen Ġthe Ġbuyer Ġgoes Ġto Ġa Ġdealership Ġto Ġbuy Ġa Ġcar , Ġthey Ġcommunicate Ġwith Ġthe Ġdealer Ġin Ġsimilar Ġcar - speak ĠB IB REF 2 Ġand Ġconvey Ġtheir Ġneeds Ġto Ġthe Ġsales Ġrepresentative . ĠFinally , Ġthe Ġsales Ġrepresentative Ġuses Ġtheir Ġinternal Ġclass ifier Ġto Ġtranslate Ġthis Ġcar - speak Ġinto Ġactual Ġphysical Ġattributes Ġ( e . g . Ġ` fast ' Ġ$ Ġ\ long right arrow Ġ$ Ġ` 700 Ġhorsepower Ġ& Ġa Ġsleek Ġform Ġfactor ') Ġand Ġoffers Ġa Ġcar Ġto Ġthe Ġbuyer . </s> Understanding Ġcar - speak Ġis Ġnot Ġa Ġtrivial Ġtask . ĠFigure ĠFIG REF 4 Ġshows Ġtwo Ġcars Ġthat Ġhave Ġhigh Ġtop Ġspeeds , Ġhowever Ġboth Ġcars Ġmay Ġnot Ġbe Ġconsidered ĠâĢ ľ fast âĢ Ŀ . ĠWe Ġneed Ġto Ġmine Ġthe Ġideas Ġthat Ġpeople Ġhave Ġabout Ġcars Ġin Ġorder Ġto Ġdetermine Ġwhich Ġcars Ġare ĠâĢ ľ fast âĢ Ŀ Ġand Ġwhich Ġcars Ġare Ġnot . </s> G athering ĠCar - speak ĠData </s> We Ġaim Ġto Ġcur ate Ġa Ġdata Ġset Ġof Ġcar - speak Ġin Ġorder Ġto Ġtrain Ġa Ġmodel Ġproperly . ĠHowever , Ġthere Ġare Ġa Ġfew Ġchallenges Ġthat Ġpresent Ġthemselves : ĠWhat Ġis Ġa Ġgood Ġsource Ġof Ġcar - speak ? ĠHow Ġcan Ġwe Ġacquire Ġthe Ġdata ? ĠHow Ġcan Ġwe Ġbe Ġsure Ġthe Ġdata Ġset Ġis Ġrelevant ? </s> What Ġis Ġa Ġgood Ġsource Ġof Ġcar - speak ? ĠWe Ġfind Ġplenty Ġof Ġcar - speak Ġin Ġcar Ġreviews . ĠTable ĠT AB REF 5 Ġprovides Ġexcerpts Ġfrom Ġreviews Ġwith Ġthe Ġcar - speak Ġterms Ġbold ed . ĠCar Ġreviews Ġoften Ġdescribe Ġcars Ġin Ġan Ġabstract Ġmanner , Ġwhich Ġmakes Ġthe Ġreview Ġmore Ġuseful Ġfor Ġcar - buy ers . ĠThe Ġreviews Ġare Ġoften Ġalso Ġabout Ġspecific Ġuse - cases Ġfor Ġeach Ġcar Ġ( e . g . Ġusing Ġthe Ġcar Ġto Ġtow Ġa Ġtrailer ), Ġso Ġthey Ġcapture Ġall Ġpossible Ġaspects Ġof Ġa Ġcar . ĠThe Ġreviews Ġare Ġeach Ġwritten Ġabout Ġa Ġspecific Ġcar , Ġso Ġwe Ġare Ġable Ġto Ġmap Ġcar - speak Ġto Ġa Ġspecific Ġcar Ġmodel . </s> We Ġchoose Ġthe Ġreviews Ġfrom Ġthe ĠU . S . ĠNews Ġ& ĠWorld ĠReport Ġbecause Ġthey Ġhave Ġeasily Ġaccessible Ġfull - length Ġreviews Ġabout Ġevery Ġcar Ġthat Ġhas Ġbeen Ġsold Ġin Ġthe ĠUnited ĠStates Ġsince Ġ2006 ĠB IB REF 5 . </s> How Ġcan Ġwe Ġacquire Ġthe Ġdata ? ĠWe Ġcan Ġacquire Ġthis Ġdata Ġusing Ġmodern Ġweb - sc rap ing Ġtools Ġsuch Ġas Ġbeautiful - s oup . ĠThe Ġdata Ġis Ġpublicly Ġavailable Ġon Ġhttps :// cars . us news . com / cars - t ru cks ĠB IB REF 5 . ĠThese Ġreviews Ġalso Ġinclude Ġa Ġscore card Ġand Ġjustification Ġof Ġtheir Ġreviews . </s> How Ġcan Ġwe Ġbe Ġsure Ġthe Ġdata Ġset Ġis Ġrelevant ? ĠOn Ġaverage Ġvehicles Ġon ĠUnited ĠStates Ġroads Ġare Ġ11 . 6 Ġyears Ġold , Ġmaking Ġthe Ġaverage Ġmanufacturing Ġyear Ġ2006 - 2007 ĠB IB REF 6 , ĠB IB REF 7 . ĠIn Ġorder Ġto Ġhave Ġa Ġrelevant Ġdata Ġset Ġwe Ġgather Ġall Ġof Ġthe Ġavailable Ġreviews Ġfor Ġcar Ġmodels Ġmade Ġbetween Ġthe Ġyears Ġ2000 Ġand Ġ2018 . </s> Trans l ating ĠCar - Spe ak </s> Our Ġdata Ġset Ġcontains Ġ$ 3 , 209 $ Ġreviews Ġabout Ġ5 53 Ġdifferent Ġcars Ġfrom Ġ49 Ġdifferent Ġcar Ġmanufacturers . ĠIn Ġorder Ġto Ġaccomplish Ġour Ġgoal Ġof Ġtranslating Ġand Ġclass ifying Ġcar - speak Ġwe Ġneed Ġto Ġfilter Ġour Ġdata Ġset Ġso Ġthat Ġwe Ġonly Ġhave Ġthe Ġmost Ġrelevant Ġterms . ĠWe Ġthen Ġneed Ġto Ġbe Ġable Ġto Ġweight Ġeach Ġword Ġin Ġeach Ġreview , Ġso Ġthat Ġwe Ġcan Ġdetermine Ġthe Ġmost Ġrelevant Ġideas Ġin Ġeach Ġdocument Ġfor Ġthe Ġpurpose Ġof Ġclassification . ĠFinally , Ġwe Ġneed Ġto Ġtrain Ġvarious Ġclassification Ġmodels Ġand Ġevaluate Ġthem . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠFil tering Ġthe ĠData </s> We Ġwould Ġlike Ġto Ġbe Ġable Ġto Ġrepresent Ġeach Ġcar Ġwith Ġthe Ġmost Ġrelevant Ġcar - speak Ġterms . ĠWe Ġcan Ġdo Ġthis Ġby Ġfiltering Ġeach Ġreview Ġusing Ġthe ĠNL T K Ġlibrary ĠB IB REF 8 , Ġonly Ġretaining Ġthe Ġmost Ġrelevant Ġwords . ĠFirst Ġwe Ġtoken - ize Ġeach Ġreview Ġand Ġthen Ġkeep Ġonly Ġthe Ġnoun s Ġand Ġadject ives Ġfrom Ġeach Ġreview Ġsince Ġthey Ġare Ġthe Ġmost Ġsalient Ġparts Ġof Ġspeech ĠB IB REF 9 . ĠThis Ġleaves Ġus Ġwith Ġ$ 10 , 8 67 $ Ġwords Ġacross Ġall Ġreviews . ĠFigure ĠFIG REF 6 Ġshows Ġthe Ġfrequency Ġof Ġthe Ġtop Ġ20 Ġwords Ġthat Ġremain . </s> Words Ġsuch Ġas ĠâĢ ľ saf te y âĢ Ŀ Ġand ĠâĢ ľ lux ury âĢ Ŀ Ġare Ġamong Ġthe Ġtop Ġwords Ġused Ġin Ġreviews . ĠThese Ġwords Ġare Ġvery Ġgood Ġexamples Ġof Ġcar - speak . ĠBoth Ġwords Ġare Ġabstract Ġdescriptions Ġof Ġcars , Ġbut Ġboth Ġhave Ġphysical Ġcharacteristics Ġthat Ġare Ġassociated Ġwith Ġthem Ġas Ġwe Ġdiscussed Ġin ĠSection ĠSEC REF 3 . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠTF - ID F </s> So Ġfar Ġwe Ġhave Ġcompiled Ġthe Ġmost Ġrelevant Ġterms Ġin Ġfrom Ġthe Ġreviews . ĠWe Ġnow Ġneed Ġto Ġweight Ġthese Ġterms Ġfor Ġeach Ġreview , Ġso Ġthat Ġwe Ġknow Ġthe Ġcar - speak Ġterms Ġare Ġmost Ġassociated Ġwith Ġa Ġcar . ĠUsing ĠTF - ID F Ġ( Term ĠFrequency - In verse ĠDocument ĠFrequency ) Ġhas Ġbeen Ġused Ġas Ġa Ġreliable Ġmetric Ġfor Ġfinding Ġthe Ġrelevant Ġterms Ġin Ġa Ġdocument ĠB IB REF 10 . </s> We Ġrepresent Ġeach Ġreview Ġas Ġa Ġvector Ġof ĠTF - ID F Ġscores Ġfor Ġeach Ġword Ġin Ġthe Ġreview . ĠThe Ġlength Ġof Ġthis Ġvector Ġis Ġ$ 10 , 8 67 $. ĠWe Ġlabel Ġeach Ġreview Ġvector Ġwith Ġthe Ġcar Ġit Ġreviews . ĠWe Ġignore Ġthe Ġyear Ġof Ġthe Ġcar Ġbeing Ġreviewed Ġand Ġfocus Ġspecifically Ġon Ġthe Ġmodel Ġ( i . e ĠAc ura ĠIL X , Ġnot Ġ2013 ĠAc ura ĠIL X ). ĠThis Ġis Ġbecause Ġthere Ġa Ġsingle Ġmodel Ġof Ġcar Ġgenerally Ġretains Ġthe Ġsame Ġcharacteristics Ġover Ġtime ĠB IB REF 11 , ĠB IB REF 12 . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠClassification ĠExper iments </s> We Ġtrain Ġa Ġseries Ġof Ġclass ifiers Ġin Ġorder Ġto Ġclassify Ġcar - speak . ĠWe Ġtrain Ġthree Ġclass ifiers Ġon Ġthe Ġreview Ġvectors Ġthat Ġwe Ġprepared Ġin ĠSection ĠSEC REF 8 . ĠThe Ġclass ifiers Ġwe Ġuse Ġare ĠK ĠNe arest ĠNeigh bors Ġ( K NN ), ĠRandom ĠForest Ġ( RF ), ĠSupport ĠVector ĠMachine Ġ( S VM ), Ġand ĠMulti - layer ĠPer cept ron Ġ( ML P ) ĠB IB REF 13 . </s> In Ġorder Ġto Ġevaluate Ġour Ġclass ifiers , Ġwe Ġperform Ġ4 - fold Ġcross Ġvalidation Ġon Ġa Ġshuff led Ġdata Ġset . ĠTable ĠT AB REF 10 Ġshows Ġthe ĠF 1 Ġmicro Ġand ĠF 1 Ġmacro Ġscores Ġfor Ġall Ġthe Ġclass ifiers . ĠThe ĠK NN Ġclass ifier Ġseem Ġto Ġperform Ġthe Ġbest Ġacross Ġall Ġfour Ġmetrics . ĠThis Ġis Ġprobably Ġdue Ġto Ġthe Ġmulti - class Ġnature Ġof Ġthe Ġdata Ġset . </s> Conclusion Ġ& ĠFuture ĠWork </s> In Ġthis Ġpaper Ġwe Ġaim Ġto Ġprovide Ġan Ġintroductory Ġunderstanding Ġof Ġcar - speak Ġand Ġa Ġway Ġto Ġautomate Ġcar Ġdealers Ġat Ġdealers hips . ĠWe Ġfirst Ġprovide Ġa Ġdefinition Ġof ĠâĢ ľ car - speak âĢ Ŀ Ġin ĠSection ĠSEC REF 3 . ĠWe Ġexplore Ġwhat Ġconstitutes Ġcar - speak Ġand Ġhow Ġto Ġidentify Ġcar - speak . </s> We Ġalso Ġgather Ġa Ġdata Ġset Ġof Ġcar - speak Ġto Ġuse Ġfor Ġexploration Ġand Ġtraining Ġpurposes . ĠThis Ġdata Ġset Ġid Ġfull Ġof Ġvehicle Ġreviews Ġfrom ĠU . S . ĠNews ĠB IB REF 5 . ĠThese Ġreviews Ġprovide Ġa Ġreasonable Ġset Ġof Ġcar - speak Ġdata Ġthat Ġwe Ġcan Ġstudy . </s> Finally , Ġwe Ġcreate Ġand Ġtest Ġseveral Ġclass ifiers Ġthat Ġare Ġtrained Ġon Ġthe Ġdata Ġwe Ġgathered . ĠWhile Ġthese Ġclass ifiers Ġdid Ġnot Ġperform Ġparticularly Ġwell , Ġthey Ġprovide Ġa Ġgood Ġstarting Ġpoint Ġfor Ġfuture Ġwork Ġon Ġthis Ġsubject . </s> In Ġthe Ġfuture Ġwe Ġplan Ġto Ġuse Ġmore Ġcomplex Ġmodels Ġto Ġattempt Ġto Ġunderstand Ġcar - speak . ĠWe Ġalso Ġwould Ġlike Ġto Ġtest Ġour Ġclass ifiers Ġon Ġuser - provided Ġnatural Ġlanguage Ġqueries . ĠThis Ġwould Ġbe Ġa Ġmore Ġpractical Ġevaluation Ġof Ġour Ġclassification . ĠIt Ġwould Ġalso Ġsatisfy Ġthe Ġneed Ġfor Ġa Ġcomputer Ġsystem Ġthat Ġunderstands Ġcar - speak .
Introduction A large portion of the car-buying experience in the United States involves interactions at a car dealership BIBREF0, BIBREF1, BIBREF2. Traditionally, a car dealer listens and understands the needs of the client and helps them find what car is right based on their needs. With the advent of the internet, many potential car buyers take to the web to research cars before going to a dealership in person BIBREF0, BIBREF2. However, nearly 50% of customers bought a car at the dealership based on the sales representative's advice, not their own research BIBREF1, BIBREF2. Throughout this interaction the dealer is acting as a type of translator or classifier. The dealer takes a natural language input (e.g. “I need a fast, family friendly, reliable car under $20k”) and returns a list of suggestions. The dealer understands the ideas of “fast”, “family friendly”, and “reliable” and is able to come up with a reasonable recommendation based on this knowledge. In this paper we aim to create a system that can understand car-speak based on some natural language input (we want to recreate the dealer from above). But how do we prepare a proper training set for a Natural Language model? What model is best suited to this problem? Can this model take a human out of the car-buying process? To answer these questions, the remainder of this paper makes the following contributions: Defining “car-speak” and its role in the car-buying process. Appropriate training data for a Natural Language model. A model that is able to properly classify car-speak and return a car. We aim to accomplish these goals in a scientific manner, using real data and modern methods. Related Work There has been some work done in the field of car-sales and dealer interactions. However, this is the first work that specifically focuses on the Deloitte has published a report on the entire car-buying process BIBREF0. The report goes into great depth about the methods potential buyers use to find new cars to buy, and how they go about buying them. The report tells us that there are several unique phases that a potential buyer goes through before buying a car. Verhoef et al. looked at the specifics of dealer interaction and how dealers retain customers BIBREF3. Verhoef tells us how important dealers are to the car-buying process. He also explains how influential a dealer can be on what car the buyer purchases. Jeff Kershner compiled a series of statistics about Dealership Sales BIBREF1. These statistics focus on small social interactions BIBREF4 between the dealer and the buyer. Barley explains the increasing role of technology in the car-buying process BIBREF2. Barley tells us that users prefer to use technology/robots to find the cars they want to buy instead of going to a dealer, due the distrust towards sales representatives. What is Car-speak? When a potential buyer begins to identify their next car-purchase they begin with identifying their needs. These needs often come in the form of an abstract situation, for instance, “I need a car that goes really fast”. This could mean that they need a car with a V8 engine type or a car that has 500 horsepower, but the buyer does not know that, all they know is that they need a “fast” car. The term “fast” is car-speak. Car-speak is abstract language that pertains to a car's physical attribute(s). In this instance the physical attributes that the term “fast” pertains to could be the horsepower, or it could be the car's form factor (how the car looks). However, we do not know exactly which attributes the term “fast” refers to. The use of car-speak is present throughout the car-buying process. It begins in the Research Phase where buyers identify their needs BIBREF0. When the buyer goes to a dealership to buy a car, they communicate with the dealer in similar car-speak BIBREF2 and convey their needs to the sales representative. Finally, the sales representative uses their internal classifier to translate this car-speak into actual physical attributes (e.g. `fast' $ \longrightarrow $ `700 horsepower & a sleek form factor') and offers a car to the buyer. Understanding car-speak is not a trivial task. Figure FIGREF4 shows two cars that have high top speeds, however both cars may not be considered “fast”. We need to mine the ideas that people have about cars in order to determine which cars are “fast” and which cars are not. Gathering Car-speak Data We aim to curate a data set of car-speak in order to train a model properly. However, there are a few challenges that present themselves: What is a good source of car-speak? How can we acquire the data? How can we be sure the data set is relevant? What is a good source of car-speak? We find plenty of car-speak in car reviews. Table TABREF5 provides excerpts from reviews with the car-speak terms bolded. Car reviews often describe cars in an abstract manner, which makes the review more useful for car-buyers. The reviews are often also about specific use-cases for each car (e.g. using the car to tow a trailer), so they capture all possible aspects of a car. The reviews are each written about a specific car, so we are able to map car-speak to a specific car model. We choose the reviews from the U.S. News & World Report because they have easily accessible full-length reviews about every car that has been sold in the United States since 2006 BIBREF5. How can we acquire the data? We can acquire this data using modern web-scraping tools such as beautiful-soup. The data is publicly available on https://cars.usnews.com/cars-trucks BIBREF5. These reviews also include a scorecard and justification of their reviews. How can we be sure the data set is relevant? On average vehicles on United States roads are 11.6 years old, making the average manufacturing year 2006-2007 BIBREF6, BIBREF7. In order to have a relevant data set we gather all of the available reviews for car models made between the years 2000 and 2018. Translating Car-Speak Our data set contains $3,209$ reviews about 553 different cars from 49 different car manufacturers. In order to accomplish our goal of translating and classifying car-speak we need to filter our data set so that we only have the most relevant terms. We then need to be able to weight each word in each review, so that we can determine the most relevant ideas in each document for the purpose of classification. Finally, we need to train various classification models and evaluate them. Translating Car-Speak ::: Filtering the Data We would like to be able to represent each car with the most relevant car-speak terms. We can do this by filtering each review using the NLTK library BIBREF8, only retaining the most relevant words. First we token-ize each review and then keep only the nouns and adjectives from each review since they are the most salient parts of speech BIBREF9. This leaves us with $10,867$ words across all reviews. Figure FIGREF6 shows the frequency of the top 20 words that remain. Words such as “saftey” and “luxury” are among the top words used in reviews. These words are very good examples of car-speak. Both words are abstract descriptions of cars, but both have physical characteristics that are associated with them as we discussed in Section SECREF3. Translating Car-Speak ::: TF-IDF So far we have compiled the most relevant terms in from the reviews. We now need to weight these terms for each review, so that we know the car-speak terms are most associated with a car. Using TF-IDF (Term Frequency-Inverse Document Frequency) has been used as a reliable metric for finding the relevant terms in a document BIBREF10. We represent each review as a vector of TF-IDF scores for each word in the review. The length of this vector is $10,867$. We label each review vector with the car it reviews. We ignore the year of the car being reviewed and focus specifically on the model (i.e Acura ILX, not 2013 Acura ILX). This is because there a single model of car generally retains the same characteristics over time BIBREF11, BIBREF12. Translating Car-Speak ::: Classification Experiments We train a series of classifiers in order to classify car-speak. We train three classifiers on the review vectors that we prepared in Section SECREF8. The classifiers we use are K Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP) BIBREF13. In order to evaluate our classifiers, we perform 4-fold cross validation on a shuffled data set. Table TABREF10 shows the F1 micro and F1 macro scores for all the classifiers. The KNN classifier seem to perform the best across all four metrics. This is probably due to the multi-class nature of the data set. Conclusion & Future Work In this paper we aim to provide an introductory understanding of car-speak and a way to automate car dealers at dealerships. We first provide a definition of “car-speak” in Section SECREF3. We explore what constitutes car-speak and how to identify car-speak. We also gather a data set of car-speak to use for exploration and training purposes. This data set id full of vehicle reviews from U.S. News BIBREF5. These reviews provide a reasonable set of car-speak data that we can study. Finally, we create and test several classifiers that are trained on the data we gathered. While these classifiers did not perform particularly well, they provide a good starting point for future work on this subject. In the future we plan to use more complex models to attempt to understand car-speak. We also would like to test our classifiers on user-provided natural language queries. This would be a more practical evaluation of our classification. It would also satisfy the need for a computer system that understands car-speak.
Is order of "words" important in car speak language?
f88036174b4a0dbf4fe70ddad884d16082c5748d
Is Ġorder Ġof Ġ" words " Ġimportant Ġin Ġcar Ġspeak Ġlanguage ?
Un answer able
<s> Is Ġorder Ġof Ġ" words " Ġimportant Ġin Ġcar Ġspeak Ġlanguage ? </s> Introduction </s> A Ġlarge Ġportion Ġof Ġthe Ġcar - bu ying Ġexperience Ġin Ġthe ĠUnited ĠStates Ġinvolves Ġinteractions Ġat Ġa Ġcar Ġdealership ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠTrad itionally , Ġa Ġcar Ġdealer Ġlistens Ġand Ġunderstands Ġthe Ġneeds Ġof Ġthe Ġclient Ġand Ġhelps Ġthem Ġfind Ġwhat Ġcar Ġis Ġright Ġbased Ġon Ġtheir Ġneeds . </s> With Ġthe Ġadvent Ġof Ġthe Ġinternet , Ġmany Ġpotential Ġcar Ġbuyers Ġtake Ġto Ġthe Ġweb Ġto Ġresearch Ġcars Ġbefore Ġgoing Ġto Ġa Ġdealership Ġin Ġperson ĠB IB REF 0 , ĠB IB REF 2 . ĠHowever , Ġnearly Ġ50 % Ġof Ġcustomers Ġbought Ġa Ġcar Ġat Ġthe Ġdealership Ġbased Ġon Ġthe Ġsales Ġrepresentative 's Ġadvice , Ġnot Ġtheir Ġown Ġresearch ĠB IB REF 1 , ĠB IB REF 2 . </s> Throughout Ġthis Ġinteraction Ġthe Ġdealer Ġis Ġacting Ġas Ġa Ġtype Ġof Ġtranslator Ġor Ġclass ifier . ĠThe Ġdealer Ġtakes Ġa Ġnatural Ġlanguage Ġinput Ġ( e . g . ĠâĢ ľ I Ġneed Ġa Ġfast , Ġfamily Ġfriendly , Ġreliable Ġcar Ġunder Ġ$ 20 k âĢ Ŀ ) Ġand Ġreturns Ġa Ġlist Ġof Ġsuggestions . ĠThe Ġdealer Ġunderstands Ġthe Ġideas Ġof ĠâĢ ľ fast âĢ Ŀ , ĠâĢ ľ family Ġfriendly âĢ Ŀ , Ġand ĠâĢ ľ rel iable âĢ Ŀ Ġand Ġis Ġable Ġto Ġcome Ġup Ġwith Ġa Ġreasonable Ġrecommendation Ġbased Ġon Ġthis Ġknowledge . </s> In Ġthis Ġpaper Ġwe Ġaim Ġto Ġcreate Ġa Ġsystem Ġthat Ġcan Ġunderstand Ġcar - speak Ġbased Ġon Ġsome Ġnatural Ġlanguage Ġinput Ġ( we Ġwant Ġto Ġrecreate Ġthe Ġdealer Ġfrom Ġabove ). ĠBut Ġhow Ġdo Ġwe Ġprepare Ġa Ġproper Ġtraining Ġset Ġfor Ġa ĠNatural ĠLanguage Ġmodel ? ĠWhat Ġmodel Ġis Ġbest Ġsuited Ġto Ġthis Ġproblem ? ĠCan Ġthis Ġmodel Ġtake Ġa Ġhuman Ġout Ġof Ġthe Ġcar - bu ying Ġprocess ? ĠTo Ġanswer Ġthese Ġquestions , Ġthe Ġremainder Ġof Ġthis Ġpaper Ġmakes Ġthe Ġfollowing Ġcontributions : </s> Def ining ĠâĢ ľ car - speak âĢ Ŀ Ġand Ġits Ġrole Ġin Ġthe Ġcar - bu ying Ġprocess . </s> App ropri ate Ġtraining Ġdata Ġfor Ġa ĠNatural ĠLanguage Ġmodel . </s> A Ġmodel Ġthat Ġis Ġable Ġto Ġproperly Ġclassify Ġcar - speak Ġand Ġreturn Ġa Ġcar . </s> We Ġaim Ġto Ġaccomplish Ġthese Ġgoals Ġin Ġa Ġscientific Ġmanner , Ġusing Ġreal Ġdata Ġand Ġmodern Ġmethods . </s> Related ĠWork </s> There Ġhas Ġbeen Ġsome Ġwork Ġdone Ġin Ġthe Ġfield Ġof Ġcar - s ales Ġand Ġdealer Ġinteractions . ĠHowever , Ġthis Ġis Ġthe Ġfirst Ġwork Ġthat Ġspecifically Ġfocuses Ġon Ġthe </s> Del o itte Ġhas Ġpublished Ġa Ġreport Ġon Ġthe Ġentire Ġcar - bu ying Ġprocess ĠB IB REF 0 . ĠThe Ġreport Ġgoes Ġinto Ġgreat Ġdepth Ġabout Ġthe Ġmethods Ġpotential Ġbuyers Ġuse Ġto Ġfind Ġnew Ġcars Ġto Ġbuy , Ġand Ġhow Ġthey Ġgo Ġabout Ġbuying Ġthem . ĠThe Ġreport Ġtells Ġus Ġthat Ġthere Ġare Ġseveral Ġunique Ġphases Ġthat Ġa Ġpotential Ġbuyer Ġgoes Ġthrough Ġbefore Ġbuying Ġa Ġcar . </s> Ver ho ef Ġet Ġal . Ġlooked Ġat Ġthe Ġspecifics Ġof Ġdealer Ġinteraction Ġand Ġhow Ġdealers Ġretain Ġcustomers ĠB IB REF 3 . ĠVer ho ef Ġtells Ġus Ġhow Ġimportant Ġdealers Ġare Ġto Ġthe Ġcar - bu ying Ġprocess . ĠHe Ġalso Ġexplains Ġhow Ġinfluential Ġa Ġdealer Ġcan Ġbe Ġon Ġwhat Ġcar Ġthe Ġbuyer Ġpurchases . </s> Jeff ĠKers hner Ġcompiled Ġa Ġseries Ġof Ġstatistics Ġabout ĠDeal ership ĠSales ĠB IB REF 1 . ĠThese Ġstatistics Ġfocus Ġon Ġsmall Ġsocial Ġinteractions ĠB IB REF 4 Ġbetween Ġthe Ġdealer Ġand Ġthe Ġbuyer . </s> Bar ley Ġexplains Ġthe Ġincreasing Ġrole Ġof Ġtechnology Ġin Ġthe Ġcar - bu ying Ġprocess ĠB IB REF 2 . ĠBar ley Ġtells Ġus Ġthat Ġusers Ġprefer Ġto Ġuse Ġtechnology / rob ots Ġto Ġfind Ġthe Ġcars Ġthey Ġwant Ġto Ġbuy Ġinstead Ġof Ġgoing Ġto Ġa Ġdealer , Ġdue Ġthe Ġdistrust Ġtowards Ġsales Ġrepresentatives . </s> What Ġis ĠCar - speak ? </s> When Ġa Ġpotential Ġbuyer Ġbegins Ġto Ġidentify Ġtheir Ġnext Ġcar - p urchase Ġthey Ġbegin Ġwith Ġidentifying Ġtheir Ġneeds . ĠThese Ġneeds Ġoften Ġcome Ġin Ġthe Ġform Ġof Ġan Ġabstract Ġsituation , Ġfor Ġinstance , ĠâĢ ľ I Ġneed Ġa Ġcar Ġthat Ġgoes Ġreally Ġfast âĢ Ŀ . ĠThis Ġcould Ġmean Ġthat Ġthey Ġneed Ġa Ġcar Ġwith Ġa ĠV 8 Ġengine Ġtype Ġor Ġa Ġcar Ġthat Ġhas Ġ500 Ġhorsepower , Ġbut Ġthe Ġbuyer Ġdoes Ġnot Ġknow Ġthat , Ġall Ġthey Ġknow Ġis Ġthat Ġthey Ġneed Ġa ĠâĢ ľ fast âĢ Ŀ Ġcar . </s> The Ġterm ĠâĢ ľ fast âĢ Ŀ Ġis Ġcar - speak . ĠCar - speak Ġis Ġabstract Ġlanguage Ġthat Ġper tains Ġto Ġa Ġcar 's Ġphysical Ġattribute ( s ). ĠIn Ġthis Ġinstance Ġthe Ġphysical Ġattributes Ġthat Ġthe Ġterm ĠâĢ ľ fast âĢ Ŀ Ġper tains Ġto Ġcould Ġbe Ġthe Ġhorsepower , Ġor Ġit Ġcould Ġbe Ġthe Ġcar 's Ġform Ġfactor Ġ( how Ġthe Ġcar Ġlooks ). ĠHowever , Ġwe Ġdo Ġnot Ġknow Ġexactly Ġwhich Ġattributes Ġthe Ġterm ĠâĢ ľ fast âĢ Ŀ Ġrefers Ġto . </s> The Ġuse Ġof Ġcar - speak Ġis Ġpresent Ġthroughout Ġthe Ġcar - bu ying Ġprocess . ĠIt Ġbegins Ġin Ġthe ĠResearch ĠPhase Ġwhere Ġbuyers Ġidentify Ġtheir Ġneeds ĠB IB REF 0 . ĠWhen Ġthe Ġbuyer Ġgoes Ġto Ġa Ġdealership Ġto Ġbuy Ġa Ġcar , Ġthey Ġcommunicate Ġwith Ġthe Ġdealer Ġin Ġsimilar Ġcar - speak ĠB IB REF 2 Ġand Ġconvey Ġtheir Ġneeds Ġto Ġthe Ġsales Ġrepresentative . ĠFinally , Ġthe Ġsales Ġrepresentative Ġuses Ġtheir Ġinternal Ġclass ifier Ġto Ġtranslate Ġthis Ġcar - speak Ġinto Ġactual Ġphysical Ġattributes Ġ( e . g . Ġ` fast ' Ġ$ Ġ\ long right arrow Ġ$ Ġ` 700 Ġhorsepower Ġ& Ġa Ġsleek Ġform Ġfactor ') Ġand Ġoffers Ġa Ġcar Ġto Ġthe Ġbuyer . </s> Understanding Ġcar - speak Ġis Ġnot Ġa Ġtrivial Ġtask . ĠFigure ĠFIG REF 4 Ġshows Ġtwo Ġcars Ġthat Ġhave Ġhigh Ġtop Ġspeeds , Ġhowever Ġboth Ġcars Ġmay Ġnot Ġbe Ġconsidered ĠâĢ ľ fast âĢ Ŀ . ĠWe Ġneed Ġto Ġmine Ġthe Ġideas Ġthat Ġpeople Ġhave Ġabout Ġcars Ġin Ġorder Ġto Ġdetermine Ġwhich Ġcars Ġare ĠâĢ ľ fast âĢ Ŀ Ġand Ġwhich Ġcars Ġare Ġnot . </s> G athering ĠCar - speak ĠData </s> We Ġaim Ġto Ġcur ate Ġa Ġdata Ġset Ġof Ġcar - speak Ġin Ġorder Ġto Ġtrain Ġa Ġmodel Ġproperly . ĠHowever , Ġthere Ġare Ġa Ġfew Ġchallenges Ġthat Ġpresent Ġthemselves : ĠWhat Ġis Ġa Ġgood Ġsource Ġof Ġcar - speak ? ĠHow Ġcan Ġwe Ġacquire Ġthe Ġdata ? ĠHow Ġcan Ġwe Ġbe Ġsure Ġthe Ġdata Ġset Ġis Ġrelevant ? </s> What Ġis Ġa Ġgood Ġsource Ġof Ġcar - speak ? ĠWe Ġfind Ġplenty Ġof Ġcar - speak Ġin Ġcar Ġreviews . ĠTable ĠT AB REF 5 Ġprovides Ġexcerpts Ġfrom Ġreviews Ġwith Ġthe Ġcar - speak Ġterms Ġbold ed . ĠCar Ġreviews Ġoften Ġdescribe Ġcars Ġin Ġan Ġabstract Ġmanner , Ġwhich Ġmakes Ġthe Ġreview Ġmore Ġuseful Ġfor Ġcar - buy ers . ĠThe Ġreviews Ġare Ġoften Ġalso Ġabout Ġspecific Ġuse - cases Ġfor Ġeach Ġcar Ġ( e . g . Ġusing Ġthe Ġcar Ġto Ġtow Ġa Ġtrailer ), Ġso Ġthey Ġcapture Ġall Ġpossible Ġaspects Ġof Ġa Ġcar . ĠThe Ġreviews Ġare Ġeach Ġwritten Ġabout Ġa Ġspecific Ġcar , Ġso Ġwe Ġare Ġable Ġto Ġmap Ġcar - speak Ġto Ġa Ġspecific Ġcar Ġmodel . </s> We Ġchoose Ġthe Ġreviews Ġfrom Ġthe ĠU . S . ĠNews Ġ& ĠWorld ĠReport Ġbecause Ġthey Ġhave Ġeasily Ġaccessible Ġfull - length Ġreviews Ġabout Ġevery Ġcar Ġthat Ġhas Ġbeen Ġsold Ġin Ġthe ĠUnited ĠStates Ġsince Ġ2006 ĠB IB REF 5 . </s> How Ġcan Ġwe Ġacquire Ġthe Ġdata ? ĠWe Ġcan Ġacquire Ġthis Ġdata Ġusing Ġmodern Ġweb - sc rap ing Ġtools Ġsuch Ġas Ġbeautiful - s oup . ĠThe Ġdata Ġis Ġpublicly Ġavailable Ġon Ġhttps :// cars . us news . com / cars - t ru cks ĠB IB REF 5 . ĠThese Ġreviews Ġalso Ġinclude Ġa Ġscore card Ġand Ġjustification Ġof Ġtheir Ġreviews . </s> How Ġcan Ġwe Ġbe Ġsure Ġthe Ġdata Ġset Ġis Ġrelevant ? ĠOn Ġaverage Ġvehicles Ġon ĠUnited ĠStates Ġroads Ġare Ġ11 . 6 Ġyears Ġold , Ġmaking Ġthe Ġaverage Ġmanufacturing Ġyear Ġ2006 - 2007 ĠB IB REF 6 , ĠB IB REF 7 . ĠIn Ġorder Ġto Ġhave Ġa Ġrelevant Ġdata Ġset Ġwe Ġgather Ġall Ġof Ġthe Ġavailable Ġreviews Ġfor Ġcar Ġmodels Ġmade Ġbetween Ġthe Ġyears Ġ2000 Ġand Ġ2018 . </s> Trans l ating ĠCar - Spe ak </s> Our Ġdata Ġset Ġcontains Ġ$ 3 , 209 $ Ġreviews Ġabout Ġ5 53 Ġdifferent Ġcars Ġfrom Ġ49 Ġdifferent Ġcar Ġmanufacturers . ĠIn Ġorder Ġto Ġaccomplish Ġour Ġgoal Ġof Ġtranslating Ġand Ġclass ifying Ġcar - speak Ġwe Ġneed Ġto Ġfilter Ġour Ġdata Ġset Ġso Ġthat Ġwe Ġonly Ġhave Ġthe Ġmost Ġrelevant Ġterms . ĠWe Ġthen Ġneed Ġto Ġbe Ġable Ġto Ġweight Ġeach Ġword Ġin Ġeach Ġreview , Ġso Ġthat Ġwe Ġcan Ġdetermine Ġthe Ġmost Ġrelevant Ġideas Ġin Ġeach Ġdocument Ġfor Ġthe Ġpurpose Ġof Ġclassification . ĠFinally , Ġwe Ġneed Ġto Ġtrain Ġvarious Ġclassification Ġmodels Ġand Ġevaluate Ġthem . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠFil tering Ġthe ĠData </s> We Ġwould Ġlike Ġto Ġbe Ġable Ġto Ġrepresent Ġeach Ġcar Ġwith Ġthe Ġmost Ġrelevant Ġcar - speak Ġterms . ĠWe Ġcan Ġdo Ġthis Ġby Ġfiltering Ġeach Ġreview Ġusing Ġthe ĠNL T K Ġlibrary ĠB IB REF 8 , Ġonly Ġretaining Ġthe Ġmost Ġrelevant Ġwords . ĠFirst Ġwe Ġtoken - ize Ġeach Ġreview Ġand Ġthen Ġkeep Ġonly Ġthe Ġnoun s Ġand Ġadject ives Ġfrom Ġeach Ġreview Ġsince Ġthey Ġare Ġthe Ġmost Ġsalient Ġparts Ġof Ġspeech ĠB IB REF 9 . ĠThis Ġleaves Ġus Ġwith Ġ$ 10 , 8 67 $ Ġwords Ġacross Ġall Ġreviews . ĠFigure ĠFIG REF 6 Ġshows Ġthe Ġfrequency Ġof Ġthe Ġtop Ġ20 Ġwords Ġthat Ġremain . </s> Words Ġsuch Ġas ĠâĢ ľ saf te y âĢ Ŀ Ġand ĠâĢ ľ lux ury âĢ Ŀ Ġare Ġamong Ġthe Ġtop Ġwords Ġused Ġin Ġreviews . ĠThese Ġwords Ġare Ġvery Ġgood Ġexamples Ġof Ġcar - speak . ĠBoth Ġwords Ġare Ġabstract Ġdescriptions Ġof Ġcars , Ġbut Ġboth Ġhave Ġphysical Ġcharacteristics Ġthat Ġare Ġassociated Ġwith Ġthem Ġas Ġwe Ġdiscussed Ġin ĠSection ĠSEC REF 3 . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠTF - ID F </s> So Ġfar Ġwe Ġhave Ġcompiled Ġthe Ġmost Ġrelevant Ġterms Ġin Ġfrom Ġthe Ġreviews . ĠWe Ġnow Ġneed Ġto Ġweight Ġthese Ġterms Ġfor Ġeach Ġreview , Ġso Ġthat Ġwe Ġknow Ġthe Ġcar - speak Ġterms Ġare Ġmost Ġassociated Ġwith Ġa Ġcar . ĠUsing ĠTF - ID F Ġ( Term ĠFrequency - In verse ĠDocument ĠFrequency ) Ġhas Ġbeen Ġused Ġas Ġa Ġreliable Ġmetric Ġfor Ġfinding Ġthe Ġrelevant Ġterms Ġin Ġa Ġdocument ĠB IB REF 10 . </s> We Ġrepresent Ġeach Ġreview Ġas Ġa Ġvector Ġof ĠTF - ID F Ġscores Ġfor Ġeach Ġword Ġin Ġthe Ġreview . ĠThe Ġlength Ġof Ġthis Ġvector Ġis Ġ$ 10 , 8 67 $. ĠWe Ġlabel Ġeach Ġreview Ġvector Ġwith Ġthe Ġcar Ġit Ġreviews . ĠWe Ġignore Ġthe Ġyear Ġof Ġthe Ġcar Ġbeing Ġreviewed Ġand Ġfocus Ġspecifically Ġon Ġthe Ġmodel Ġ( i . e ĠAc ura ĠIL X , Ġnot Ġ2013 ĠAc ura ĠIL X ). ĠThis Ġis Ġbecause Ġthere Ġa Ġsingle Ġmodel Ġof Ġcar Ġgenerally Ġretains Ġthe Ġsame Ġcharacteristics Ġover Ġtime ĠB IB REF 11 , ĠB IB REF 12 . </s> Trans l ating ĠCar - Spe ak Ġ: :: ĠClassification ĠExper iments </s> We Ġtrain Ġa Ġseries Ġof Ġclass ifiers Ġin Ġorder Ġto Ġclassify Ġcar - speak . ĠWe Ġtrain Ġthree Ġclass ifiers Ġon Ġthe Ġreview Ġvectors Ġthat Ġwe Ġprepared Ġin ĠSection ĠSEC REF 8 . ĠThe Ġclass ifiers Ġwe Ġuse Ġare ĠK ĠNe arest ĠNeigh bors Ġ( K NN ), ĠRandom ĠForest Ġ( RF ), ĠSupport ĠVector ĠMachine Ġ( S VM ), Ġand ĠMulti - layer ĠPer cept ron Ġ( ML P ) ĠB IB REF 13 . </s> In Ġorder Ġto Ġevaluate Ġour Ġclass ifiers , Ġwe Ġperform Ġ4 - fold Ġcross Ġvalidation Ġon Ġa Ġshuff led Ġdata Ġset . ĠTable ĠT AB REF 10 Ġshows Ġthe ĠF 1 Ġmicro Ġand ĠF 1 Ġmacro Ġscores Ġfor Ġall Ġthe Ġclass ifiers . ĠThe ĠK NN Ġclass ifier Ġseem Ġto Ġperform Ġthe Ġbest Ġacross Ġall Ġfour Ġmetrics . ĠThis Ġis Ġprobably Ġdue Ġto Ġthe Ġmulti - class Ġnature Ġof Ġthe Ġdata Ġset . </s> Conclusion Ġ& ĠFuture ĠWork </s> In Ġthis Ġpaper Ġwe Ġaim Ġto Ġprovide Ġan Ġintroductory Ġunderstanding Ġof Ġcar - speak Ġand Ġa Ġway Ġto Ġautomate Ġcar Ġdealers Ġat Ġdealers hips . ĠWe Ġfirst Ġprovide Ġa Ġdefinition Ġof ĠâĢ ľ car - speak âĢ Ŀ Ġin ĠSection ĠSEC REF 3 . ĠWe Ġexplore Ġwhat Ġconstitutes Ġcar - speak Ġand Ġhow Ġto Ġidentify Ġcar - speak . </s> We Ġalso Ġgather Ġa Ġdata Ġset Ġof Ġcar - speak Ġto Ġuse Ġfor Ġexploration Ġand Ġtraining Ġpurposes . ĠThis Ġdata Ġset Ġid Ġfull Ġof Ġvehicle Ġreviews Ġfrom ĠU . S . ĠNews ĠB IB REF 5 . ĠThese Ġreviews Ġprovide Ġa Ġreasonable Ġset Ġof Ġcar - speak Ġdata Ġthat Ġwe Ġcan Ġstudy . </s> Finally , Ġwe Ġcreate Ġand Ġtest Ġseveral Ġclass ifiers Ġthat Ġare Ġtrained Ġon Ġthe Ġdata Ġwe Ġgathered . ĠWhile Ġthese Ġclass ifiers Ġdid Ġnot Ġperform Ġparticularly Ġwell , Ġthey Ġprovide Ġa Ġgood Ġstarting Ġpoint Ġfor Ġfuture Ġwork Ġon Ġthis Ġsubject . </s> In Ġthe Ġfuture Ġwe Ġplan Ġto Ġuse Ġmore Ġcomplex Ġmodels Ġto Ġattempt Ġto Ġunderstand Ġcar - speak . ĠWe Ġalso Ġwould Ġlike Ġto Ġtest Ġour Ġclass ifiers Ġon Ġuser - provided Ġnatural Ġlanguage Ġqueries . ĠThis Ġwould Ġbe Ġa Ġmore Ġpractical Ġevaluation Ġof Ġour Ġclassification . ĠIt Ġwould Ġalso Ġsatisfy Ġthe Ġneed Ġfor Ġa Ġcomputer Ġsystem Ġthat Ġunderstands Ġcar - speak .