1 / 11

Exploring Comedic Responses: Crafting Humor through Data Analysis

Join Mike Cialowicz, Zeid Rusan, Matt Gamble, and Keith Harris as they embark on a hilarious mission to explore strange new worlds of comedy! This project aims to generate the funniest possible responses to given sentences through comedy data analysis. By understanding the relationships between words and the structures of jokes, they break down humor into a science. With a blend of innovation and wit, witness how humor is quantified, smoothed, and ultimately transformed into engaging zingers. Prepare for a laugh!

ryu
Download Presentation

Exploring Comedic Responses: Crafting Humor through Data Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Funny Factory Mike Cialowicz Zeid Rusan Matt Gamble Keith Harris

  2. Our Missions: 1- To explore strange new worlds. 2- Given an inputed sentence, output the statistically funniest response based on comedic data. Our Approach: 1- Learn from relationships between words in jokes. 2- Learn from sentence structures of jokes. “On Screen!”

  3. Setup 1: “I feel bad going behind Lois' back.” Setup 2: “Don't feel bad Peter.” Zinger!: “Oh I never thought of it like that!” Step 1: Collect data (2.5 MB) . . . . . .

  4. “I feel bad going behind Lois' back.” “Don't feel bad Peter.” /VB /NN /JJ /NNP “Oh I never thought of it like that!” /UH /PRP /RB /VBD /IN /PRP /IN /DT Step 2: Tag the jokes (Size = 3.5MB) /PRP /VBP /JJ /NN /IN /NNP /RB Attach: Attach: Attach: “Who tagged that there?”

  5. I feel bad going behind Lois' back Step 3a: Zinger word counts(100 MB) For each word : Count! For word 'feel' : Intuition: Word relations in Zingers should help us construct our own!

  6. Step 3b: Cross sentence counts (## MB) For each adjacent pair in setups : Don't feel bad Peter Oh I never thought of it like that! Count! : For 'feel,bad ' : Intuition: Words in input should help us place a seed word in Zingers we are constructing!

  7. Oh I never thought of it like that! /UH /PRP /RB /VBD /IN /PRP /IN /DT Step 3c: Structure counts (2.2 MB) For each sentence : Count! : Intuition: Using known funny Zinger structures should yield funnier constructed Zingers.

  8. Converted dictionary counts to probabilities using: Laplace smoothing (k = 1) Lidstone's law (k = 0.5, 0.05) Step 4: Smoothing! “Damn that's smooth”

  9. This is an example sense makes sense /DT makes sense “This makes sense” Step 5: Make a sentence! Input sentence : Get seed word : Highest Prob Generate more words : Highest Prob Get a structure : Highest Prob Complete sentence : Highest Prob

  10. Step 6: DEMO! 5/11/2006 @ 4:13 am in the Linux Lab “YEAH BOYYYYYYYY!”

  11. - Incorporate semantics. - Collect MORE data. (Need a better computer) - Apply weights to cross sentence counts - Evaluate using test subjects (mainly Billy) with different combinations of weight and probability (k = #) parameters. - Do parameters converge along with funny? - Reevaluate using the (better?) parameters. Step 7: Future Work

More Related