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Opening Computational Door on Knock Knock Jokes Julia M. Taylor & Lawrence J. Mazlack Applied Artificial Intelligence Laboratory University of Cincinnati Introduction This is an initial investigation into computational humor recognition using wordplay The program

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Opening Computational Door on Knock Knock Jokes


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opening computational door on knock knock jokes

Opening Computational Door on Knock Knock Jokes

Julia M. Taylor & Lawrence J. Mazlack

Applied Artificial Intelligence Laboratory

University of Cincinnati

introduction
Introduction
  • This is an initial investigation into computational humor recognition using wordplay
  • The program
    • Learns statistical patterns of text
    • Recognizes utterances similar in pronunciation to a given word
    • Determines if found utterances transform a text into a joke
restricted domain knock knock jokes
Restricted Domain: Knock Knock Jokes
  • Line1: “Knock, Knock”
  • Line2: “Who’s there?”
  • Line3: any phrase
  • Line4: Line3 followed by “who?”
  • Line5: One or several sentences containing
    • Type1: Line3
    • Type2: A wordplay on Line3
    • Type3: A meaningful response to a wordplay of Line3 or Line4
restricted domain knock knock jokes4
Restricted Domain: Knock Knock Jokes
  • Type1: Line3

--Knock, Knock

--Who’s there?

--Water

--Water who?

--Water you doing tonight?

  • Type2: A wordplay on Line3

--Knock, Knock

--Who’s there?

--Ashley

--Ashley who?

--Actually, I don’t know.

  • Type3: A meaningful response to a wordplay of Line4

--Knock, Knock

--Who’s there?

--Tank

--Tank who?

--You are welcome.

experimental design
Experimental Design
  • Training set:
    • 66 Knock Knock jokes
    • Enhance similarity table of letters
    • Select N-gram training texts
      • 66 texts containing wordplay from 66 training jokes
  • Test set:
    • 130 Knock Knock jokes
    • 66 Non-jokes that have similar structure to Knock Knock jokes
similarity table
Similarity Table
  • Contains combination of letters that sound similar
  • Based on similarity table of cross-referenced English consonant pairs
  • Modified by:
    • translating phonemes to letters
    • adding vowels that are close in sound
    • adding other combinations of letters that may be used to recognize wordplay

Segment of similarity table

training corpus
Training Corpus
  • Training texts were entered into N-gram database

Nurse: I need to get your weight today.

Impatient patient: 3 hours and 45 minutes.

    • Wordplay validation: bigram table

(I need 1) (need to 1) (to get 1) (get your 1) (your weight 1) (weight today 1) (today end-of-sentence 1)

    • Punchline validation: trigram table

(I need to 1) (need to get 1) (to get your 1) (get your weight 1) (your weight today 1) (weight todayend-of-sentence1)

how it works
How It Works
  • Step1: joke format validation
  • Step2: computational generation of sound-alike sequences
  • Step3: validations of a chosen sound-alike sequence
  • Step4: last sentence validation with sound-alike sequence
step 1 joke format validation
Step 1: Joke Format Validation
  • Line1: “Knock, Knock”
  • Line2: “Who’s there?”
  • Line3: any phrase
  • Line4: Line3 followed by “who?”
  • Line5: One or several sentences containing Line3
  • Knock, Knock
  • Who is there?
  • I, Felix
  • I, Felix who?
  • I, Felix-ited!
  • Knock, Knock
  • Who is there?
  • I, Felix
  • I, Felix who?
  • I feelexcited!
step 2 generation of wordplay sequences
Step 2: Generation of Wordplay Sequences
  • Repetitive letter replacements of Line3
  • Similarity used for letter replacements
  • Resulting utterances are ordered according to their similarity with Line3
  • Utterances with highest similarity are checked for decomposition into several words

Segment of similarity table

step 2 generation of wordplay sequences11
Step 2: Generation of Wordplay Sequences

ifelixited

10.0

efelixited

9.23

step 2 generation of wordplay sequences12
Step 2: Generation of Wordplay Sequences

ifelixited

10.0

efelixited

9.23

ifilixited

9.23

ifalixited

9.23

ifolixited

9.23

step 2 generation of wordplay sequences13
Step 2: Generation of Wordplay Sequences

ifelixited

10.0

efelixited

9.23

ifilixited

9.23

ifalixited

9.23

ifolixited

9.23

iferixited

9.56

step 2 generation of wordplay sequences14
Step 2: Generation of Wordplay Sequences

ifelixited

10.0

efelixited

9.23

ifilixited

9.23

iferixited

9.56

ifolixited

9.23

ifalixited

9.23

step 2 generation of wordplay sequences15
Step 2: Generation of Wordplay Sequences

ifelixited

10.0

iferixited

9.56

ifilixited

9.23

efelixited

9.23

ifolixited

9.23

ifalixited

9.23

step 2 generation of wordplay sequences16
Step 2: Generation of Wordplay Sequences

if el exited

ifelexited

9.23

***

***

***

***

step 3 wordplay validation
Step 3: Wordplay Validation

if el exited

NO

one word?

YES

divide into pairs

if el

el exited

each pair in bigram?

NO

YES

Step 2

Step 4

step 2 generation of wordplay sequences18
Step 2: Generation of Wordplay Sequences

i feel excited

ifeelexcited

7.x

***

***

***

***

step 3 wordplay validation19
Step 3: Wordplay Validation

i feel excited

NO

one word?

YES

divide into pairs

I feel

feel excited

each pair in bigram?

NO

YES

Step 2

Step 4

step 4 last sentence validation with wordplay
Step 4: Last SentenceValidation with Wordplay
  • Wordplay is meaningful
  • Could occur
    • In the beginning of last sentence
    • In the middle of last sentence
    • At the end of last sentence
step 4 last sentence validation with wordplay21
Step 4: Last Sentence Validation with Wordplay
  • In the beginning of sentence:

i feel excited

(wordplay N, punch1, punch2)

in trigram?

NO

YES

One word?

YES

NO

(wordplay N-1, wordplay N, punch1)

in trigram?

NO

YES

Step 2

joke

Step 2

step 4 last sentence generation with wordplay
Step 4: Last Sentence Generation with Wordplay
  • In the beginning of sentence:

i feel excited

Is there sentence in the

training text with wordplay?

NO

YES

Step 2

joke

knock knock joke generation
Knock Knock Joke Generation

--Knock, Knock

--Who’s there?

--Ammonia

--Ammonia who?

--Ammonia …

Ammonia = I’m only

  • --Knock, Knock
  • --Who’s there?
  • --Ammonia
  • --Ammonia who?
  • --Ammonia trying to be funny
results
Results
  • 66 training jokes
    • 59 jokes were recognized
    • 7 unrecognized, no wordplay found
  • 66 non-jokes
    • 62 correctly recognized as non-jokes
    • 1 found wordplay that makes sense
    • 3 incorrectly recognized as jokes
  • 130 test jokes
    • 8 jokes were not expected to be recognized
    • 12 identified as jokes with expected wordplay
    • 5 identified as jokes with unexpected wordplay
    • 80 expected wordplays found
possible enhancements
Possible Enhancements
  • Improve last sentence validation
    • Increasing size of text used for N-gram training
    • Parser
    • N-grams with stemming
  • Improve wordplay generator
    • Use of phoneme comparison
  • Use wider domain
    • All types of Knock Knock jokes
    • Other types of wordplay jokes
conclusion
Conclusion
  • Initial investigation into KK joke recognition using wordplay
  • The program was designed to
    • Recognize wordplay in KK jokes 67%
    • Recognize KK jokes containing wordplay 12%
  • Alternate result of this program
    • KK joke generator