1 / 14

Recognizing Literary Tropes in Plot Synopses

Recognizing Literary Tropes in Plot Synopses. Sam Carton April 12, 2013. Outline. Introduction Data Method Experiment Results Conclusion. Data – TVTropes.org. TVTropes.org ~30,000 tropes overall 6,583 pages for films, books, video games

kyna
Télécharger la présentation

Recognizing Literary Tropes in Plot Synopses

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. Recognizing Literary Tropes in Plot Synopses Sam Carton April 12, 2013

  2. Outline • Introduction • Data • Method • Experiment • Results • Conclusion

  3. Data – TVTropes.org • TVTropes.org • ~30,000 tropes overall • 6,583 pages for films, books, video games • 983 tropes that occur >50 times in that collection

  4. Data - Wikipedia • ~40,000,000 English-language pages • 41 gigs uncompressed text

  5. Data - Disambiguation • Non-trivial ?

  6. Method – Text pre-processing

  7. Method – N-grams • Instead of individual words, look at sequences of words • Unigram • After witnessing the power of their guns , the human tribe does not want the expedition to leave , and tries to keep them on the plateau .“BECOMES[After],[witnessing],[the],[power]… • Bigram • After witnessing the power of their guns , the human tribe does not want the expedition to leave , and tries to keep them on the plateau .“BECOMES[After witnessing], [witnessing the], [the power], [power of]…

  8. Experiment • Main Experiment • Performance of N-gram/preprocessing combinations • Trope frequency experiment • Performance of models on more-frequent/less-frequent tropes • Media type experiment • Performance of models on different media types

  9. Results – Main 1

  10. Results – Main 2

  11. Results – Trope frequency

  12. Results – Media types

  13. Conclusion • Hard problem • More data needed • Positive examples • More sophisticated models • One size fits all?

  14. Future work • Additional text data • Consider inter-trope correlations • More media types

More Related