|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
A Hierarchical Bayesian Language Model based on Pitman-Yor Processes
If you have a question about this talk, please contact Shakir Mohamed.
I will be discussing:
N-gram language modelling traditionally uses some form of “smoothing” technique to allocate some probability mass to unseen N-grams. Over the years people have come up with smoothing schemes that perform pretty well, but it’s not easy to get a handle on what they’re doing, and how to improve them.
In this paper, Teh shows that a hierarchical Bayesian language model with a very simplistic model of context performs pretty much as well as the current state of the art smoothing schemes, and in fact has strong similarities to an existing smoothing scheme.
This talk is part of the Machine Learning Reading Group @ CUED series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsClare Hall Seminars on Quantitative Biology @ CRUK Cambridge Institute Cambridge Psychometrics Centre Seminars
Other talksFrom Sensory Perception to Foraging Decision Making, the Bat's Point of View Will Migration spell the end of Europe? Regulation of mitochondrial function by stearic acid Head and neck cancer: insights into aetiology and prognosis from a clinical cohort The transparent editorial process, data reproducibility and research integrity at EMBO Press Unravel buried soft interfaces: From lipid bilayers to responsive polymer brushes