[Ml-stat-talks] Fwd: Percy Liang, Wed. April 6, 4:30PM

David Blei blei at CS.Princeton.EDU
Tue Apr 5 18:00:46 EDT 2011

hi ml-stat-talks

though the subject was correct, below i announced percy's talk for
thursday.  it is WEDNESDAY, tomorrow.  don't miss it!


---------- Forwarded message ----------
> hi ml-stat-talks
> next thursday's colloquium speaker is percy liang.  percy is a
> fantastic researcher and speaker.  as usual, i mean it: this is not to
> be missed!
> best
> dave
> ---------- Forwarded message ----------
> From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
> Date: Fri, Apr 1, 2011 at 9:02 AM
> Subject: [talks] Colloquium Speaker, Percy Liang, Wed. April 6
> To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>
> Deep Semantics from Shallow Supervision
> Percy Liang, UC Berkeley
> Wednesday April 6, 4:30pm
> Computer Science 105
> What is the total population of the ten largest capitals in the US?
> Building a system to answer free-form questions such as this requires
> modeling the deep semantics of language. But to develop practical,
> scalable systems, we want to avoid the costly manual annotation of
> these deep semantic structures and instead learn from just
> surface-level supervision, e.g., question/answer pairs. To this end,
> we develop a new tree-based semantic representation which has
> favorable linguistic and computational properties, along with an
> algorithm that induces this hidden representation. Using our approach,
> we obtain significantly higher accuracy on the task of question
> answering compared to existing state-of-the-art methods, despite using
> less supervision.
> Percy Liang obtained a B.S. (2004) and an M.S. (2005) from MIT and is
> now completing his Ph.D. at UC Berkeley with Michael Jordan and Dan
> Klein. The general theme of his research, which spans machine learning
> and natural language processing, is learning richly-structured
> statistical models from limited supervision. He has won a best student
> paper at the International Conference on Machine Learning in 2008,
> received the NSF, GAANN, and NDSEG fellowships, and is also a 2010
> Siebel Scholar.
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