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Prospects for In-depth Story Understanding by Computer

by: Erik T. Mueller

(2000) .

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URL:	http://arXiv.org/abs/cs/0003003

While much research on the hard problem of in-depth story understanding by computer was performed starting in the 1970s, interest shifted in the 1990s to information extraction and word sense disambiguation. Now that a degree of success has been achieved on these easier problems, I propose it is time to return to in-depth story understanding. In this paper I examine the shift away from story understanding, discuss some of the major problems in building a story understanding system, present some possible solutions involving a set of interacting understanding agents, and provide pointers to useful tools and resources for building story understanding systems.

BibTeX record

@misc{Mueller:2000,
  abstract = {While much research on the hard problem of in-depth story understanding by computer was performed starting in the 1970s, interest shifted in the 1990s to information extraction and word sense disambiguation. Now that a degree of success has been achieved on these easier problems, I propose it is time to return to in-depth story understanding. In this paper I examine the shift away from story understanding, discuss some of the major problems in building a story understanding system, present some possible solutions involving a set of interacting understanding agents, and provide pointers to useful tools and resources for building story understanding systems. },
  added-at = {2007-12-14T02:44:17.000+0100},
  author = {Mueller, Erik T.},
  biburl = {http://www.bibsonomy.org/bibtex/2e7e0fa54c01bf4939fe8d73aa6af57fd/diego_ma},
  interhash = {4d927440f43a655845caa3f83478139f},
  intrahash = {e7e0fa54c01bf4939fe8d73aa6af57fd},
  keywords = {AI NLP},
  note = {\myurl{http://arXiv.org/abs/cs/0003003}},
  timestamp = {2007-12-14T02:44:17.000+0100},
  title = {Prospects for In-depth Story Understanding by Computer},
  url = {http://arXiv.org/abs/cs/0003003},
  year = 2000
}

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BibSonomy :: publication ::

Prospects for In-depth Story Understanding by Computer

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Abstract

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