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Identifiability and Unmixing of Latent Parse Trees
Percy Liang · Sham M Kakade · Daniel Hsu

Wed Dec 05 07:00 PM -- 11:59 PM (PST) @ Harrah’s Special Events Center 2nd Floor

This paper explores unsupervised learning of parsing models along two directions. First, which models are identifiable from infinite data? We use a general technique for numerically checking identifiability based on the rank of a Jacobian matrix, and apply it to several standard constituency and dependency parsing models. Second, for identifiable models, how do we estimate the parameters efficiently? EM suffers from local optima, while recent work using spectral methods cannot be directly applied since the topology of the parse tree varies across sentences. We develop a strategy, unmixing, which deals with this additional complexity for restricted classes of parsing models.

Author Information

Percy Liang (Stanford University)
Sham M Kakade (Harvard University & Microsoft Research)
Daniel Hsu (Columbia University)

See <https://www.cs.columbia.edu/~djhsu/>

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