Question 1

For each of the sentence fragments below use your natural language processing algorithm to predict the next word in the sentence.

The guy in front of me just bought a pound of bacon, a bouquet, and a case of

☐ soda

☐ cheese

☑ beer

☐ pretzels

##   predictor outcome           mle
## 1 case of   beer     1.384083e-02
## 2 cases of  beer     1.315789e-02
## 3 of        beer     5.267761e-04
## 4 of        cheese   1.789051e-04
## 5 of        beers    1.192701e-04
## 6 of        soda     6.957421e-05
## 7 of        cheeses  1.490876e-05
## 8 of        pretzels 9.939172e-06
## 9 of        sodas    4.969586e-06

Question 2

You’re the reason why I smile everyday. Can you follow me please? It would mean the

☐ most

☐ universe

☑ world

☐ best

##    predictor outcome               mle
## 1  mean the  world        2.260274e-01
## 2  means the world        8.771930e-02
## 3  means the universe     8.771930e-03
## 4  the       best         8.481113e-03
## 5  the       world        6.982350e-03
## 6  mean the  universe     6.849315e-03
## 7  the       university   1.287050e-03
## 8  the       worlds       7.630067e-04
## 9  the       universe     3.668302e-04
## 10 the       universitys  7.546221e-05
## 11 the       universal    3.353876e-05
## 12 the       mostly       2.305790e-05
## 13 the       universities 1.886555e-05
## 14 the       universes    4.192345e-06
## 15 the       universalism 2.096172e-06
## 16 the       universality 2.096172e-06
## 17 the       universals   2.096172e-06

Question 3

Hey sunshine, can you follow me and make me the

☐ smelliest

☐ saddest

☐ bluest

☑ happiest

##   predictor outcome           mle
## 1 me the    happiest 7.680492e-03
## 2 the       happiest 9.852010e-05
## 3 the       saddest  2.515407e-05
## 4 the       bluest   1.048086e-05

Question 4

Very early observations on the Bills game: Offense still struggling but the

☐ players

☐ referees

☑ defense

☐ crowd

##    predictor outcome              mle
## 1  but the   crowd       3.215434e-03
## 2  but the   defense     3.215434e-03
## 3  but the   defensive   3.215434e-03
## 4  the       crowd       6.534983e-04
## 5  the       players     6.534983e-04
## 6  the       defense     3.372895e-04
## 7  the       player      1.897253e-04
## 8  the       defensive   1.475641e-04
## 9  the       crowds      4.216118e-05
## 10 the       crowded     2.108059e-05
## 11 the       defenses    2.108059e-05
## 12 the       defensively 2.108059e-05

Question 5

Go on a romantic date at the

☐ movies

☐ mall

☑ beach

☐ grocery

##    predictor outcome          mle
## 1  at the    beach   2.842928e-03
## 2  at the    mall    2.842928e-03
## 3  at the    grocery 1.421464e-03
## 4  at the    movies  1.421464e-03
## 5  the       movie   9.486266e-04
## 6  the       beach   7.378207e-04
## 7  the       movies  3.372895e-04
## 8  the       grocery 2.529671e-04
## 9  the       mall    2.529671e-04
## 10 the       beaches 2.108059e-05

Question 6

Well I’m pretty sure my granny has some old bagpipes in her garage I’ll dust them off and be on my

☐ phone

☑ way

☐ motorcycle

☐ horse

##    predictor outcome             mle
## 1  on my     way        8.106267e-02
## 2  on my     phone      1.941417e-02
## 3  my        way        8.328753e-03
## 4  my        phone      6.879550e-03
## 5  on my     horse      3.405995e-04
## 6  my        phones     2.332051e-04
## 7  my        horse      2.332051e-04
## 8  my        ways       8.328753e-05
## 9  my        horses     6.663002e-05
## 10 my        phonee     3.331501e-05
## 11 my        motorcycle 1.665751e-05

Question 7

Ohhhhh #PointBreak is on tomorrow. Love that film and haven’t seen it in quite some

☐ weeks

☑ time

☐ thing

☐ years

##    predictor  outcome          mle
## 1  quite some time    9.148936e-01
## 2  quite some things  2.127660e-02
## 3  quite some years   2.127660e-02
## 4  some       time    1.937812e-02
## 5  some       things  8.206041e-03
## 6  some       years   2.323397e-03
## 7  some       times   1.087548e-03
## 8  some       weeks   9.886796e-04
## 9  some       thing   4.943398e-04
## 10 some       week    9.886796e-05
## 11 some       year    4.943398e-05

Question 8

After the ice bucket challenge Louis will push his long wet hair out of his eyes with his little

☐ eyes

☑ fingers

☐ ears

☐ toes

##   predictor  outcome          mle
## 1 his little finger  0.0833333333
## 2 little     finger  0.0004820439
## 3 little     fingers 0.0002410219
## 4 little     toe     0.0002410219
## 5 little     toes    0.0002410219
## 6 little     ears    0.0001205110
## 7 little     eye     0.0001205110

Question 9

Be grateful for the good times and keep the faith during the

☑ bad

☐ hard

☐ worse

☐ sad

##   predictor outcome           mle
## 1 the       bad      4.590618e-04
## 2 the       hard     3.647340e-04
## 3 the       sad      1.090010e-04
## 4 the       worse    6.707752e-05
## 5 the       sadness  2.515407e-05
## 6 the       badly    4.192345e-06
## 7 the       hardly   2.096172e-06
## 8 the       hardness 2.096172e-06
## 9 the       sadly    2.096172e-06

Question 10

If this isn’t the cutest thing you’ve ever seen, then you must be

☐ asleep

☐ insensitive

☐ callous

☑ insane

##   predictor outcome              mle
## 1 must be   insane      9.541985e-04
## 2 be        insane      1.824651e-04
## 3 be        asleep      1.459721e-04
## 4 being     insane      1.166181e-04
## 5 being     insensitive 1.166181e-04
## 6 be        insensitive 3.649302e-05
## 7 be        insanely    1.824651e-05