On the Parameter Space of Lexicalized Statistical Parsing Models – Dan Bikel (IBM)

October 25, 2005 all-day

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Over the last several years, lexicalized statistical parsing models have been hitting a “rubber ceiling” when it comes to overall parse accuracy. These models have become increasingly complex, and therefore require thorough scrutiny, both to achieve the scientific aim of understanding what has been built thus far, and to achieve both the scientific and engineering goal of using that understanding for progress. In this talk, I will discuss how I have applied as well as developed techniques and methodologies for the examination of the complex systems that are lexicalized statistical parsing models. The primary idea is that of treating the “model as data”, which is not a particular method, but a paradigm and a research methodology. Accordingly, I take a particular, dominant type of parsing model and perform a macro analysis, to reveal its core and design a software engine that modularizes the periphery, and I also crucially perform a detailed analysis, which provides for the first time a window onto the efficacy of specific parameters. These analyses have not only yielded insight into the core model, but they have also enabled the identification of “inefficiencies” in my baseline model, such that those inefficiencies can be reduced to form a more compact model, or exploited for finding a better-estimated model with higher accuracy, or both.

Daniel M. Bikel graduated from Harvard University in 1993 with an A.B. in Classics–Greek & Latin. After spending a post-graduate year at Harvard taking more courses in computer science, engineering and music, Bikel joined Ralph Weischedels research group at BBN in Cambridge, MA. During his three years there, Bikel developed several NLP technologies, including Nymble now called IdentiFinder, a learning named-entity detector. In 2004, Bikel received a Ph.D. from the Computer and Information Science Department at the University of Pennsylvania (advisor: Prof. Mitch Marcus). At Penn, he focused on statistical natural language parsing, culminating in a dissertation entitled identically to this talk. Bikel is currently a Research Staff Member at IBMs T. J. Watson Research Center in Yorktown Heights, NY.

Center for Language and Speech Processing