%0 Generic %1 Griffiths2007 %A Griffiths, Thomas L. %A Steyvers, Mark %A Tenenbaum, Joshua B. %D 2007 %K information lda leakage networks seminar social talk ws2012 hidden %T Topics in semantic representation %X Accounts of language processing have suggested that it requires retrieving concepts from memory in response to an ongoing stream of information. This can be facilitated by inferring the gist of a sentence, conversation, or document, and using that gist to predict related concepts and disambiguate words. We analyze the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads us to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

@misc{Griffiths2007, abstract = {Accounts of language processing have suggested that it requires retrieving concepts from memory in response to an ongoing stream of information. This can be facilitated by inferring the gist of a sentence, conversation, or document, and using that gist to predict related concepts and disambiguate words. We analyze the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads us to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure. }, added-at = {2012-11-10T23:09:22.000+0100}, author = {Griffiths, Thomas L. and Steyvers, Mark and Tenenbaum, Joshua B.}, biburl = {https://www.bibsonomy.org/bibtex/2984772b06451984be0027b0a190c1d86/alexsgz}, interhash = {0d6d9f12fba1702e363f6e0b5de1f011}, intrahash = {984772b06451984be0027b0a190c1d86}, keywords = {information lda leakage networks seminar social talk ws2012 hidden}, timestamp = {2012-11-12T15:32:20.000+0100}, title = {Topics in semantic representation}, year = 2007 }