That's what she said: Double entendre identification (bibtex)

by Chloé Kiddon and Yuriy Brun

Abstract:

Humor identification is a hard natural language understanding problem. We identify a subproblem — the "that's what she said" problem — with two distinguishing characteristics: (1) use of nouns that are euphemisms for sexually explicit nouns and (2) structure common in the erotic domain. We address this problem in a classification approach that includes features that model those two characteristics. Experiments on web data demonstrate that our approach improves precision by 12% over baseline techniques that use only word-based features.

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Citation:

Chloé Kiddon and Yuriy Brun, That's what she said: Double entendre identification, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), 2011, pp. 89–94.

Bibtex Entry:

@inproceedings{Kiddon11,
  author = {Chlo{\'{e}} Kiddon and Yuriy Brun},
  title = {That's
  what she said: Double entendre identification},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for
  Computational Linguistics (ACL)},
  venue = {ACL},
  year = {2011},
  month = {June},
  date = {19--24},
  pages = {89--94},
  address = {Portland, {OR}, {USA}},
  url = {http://dl.acm.org/citation.cfm?id=2002756},

  note = {ACM ID: 2002756},

  abstract = {Humor identification is a hard natural language understanding
  problem. We identify a subproblem --- the "that's what she said" problem ---
  with two distinguishing characteristics: (1) use of nouns that are
  euphemisms for sexually explicit nouns and (2) structure common in the
  erotic domain. We address this problem in a classification approach that
  includes features that model those two characteristics. Experiments on web
  data demonstrate that our approach improves precision by 12\% over baseline
  techniques that use only word-based features.},

  fundedBy = {NSF CNS-0937060 to the CRA for the CIFellows Project,
  NSF DGE-0718124},
}