Statistical methods in language processing

The term statistical methods here refers to a methodology that has been dominant in computational linguistics since about 1990. It is characterized by the use of stochastic models, substantial data sets, machine learning, and rigorous experimental evaluation. The shift to statistical methods in computational linguistics parallels a movement in artificial intelligence more broadly. Statistical methods have so thoroughly permeated computational linguistics that almost all work in the field draws on them in some way. There has, however, been little penetration of the methods into general linguistics. The methods themselves are largely borrowed from machine learning and information theory. We limit attention to that which has direct applicability to language processing, though the methods are quite general and have many nonlinguistic applications. Not every use of statistics in language processing falls under statistical methods as we use the term. Standard hypothesis testing and experimental design, for example, are not covered in this article. WIREs Cogni Sci 2011 2 315–322 DOI: 10.1002/wcs.111 For further resources related to this article, please visit the WIREs websitePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83468/1/111_ftp.pd

Interferometric Observations of Rapidly Rotating Stars

Optical interferometry provides us with a unique opportunity to improve our understanding of stellar structure and evolution. Through direct observation of rotationally distorted photospheres at sub-milliarcsecond scales, we are now able to characterize latitude dependencies of stellar radius, temperature structure, and even energy transport. These detailed new views of stars are leading to revised thinking in a broad array of associated topics, such as spectroscopy, stellar evolution, and exoplanet detection. As newly advanced techniques and instrumentation mature, this topic in astronomy is poised to greatly expand in depth and influence.Comment: Accepted for publication in A&AR

Reanalysis and Limited Repair Parsing: Leaping off the Garden Path

This chapter develops a theory of reanalysis called limited repair parsing. Repair parsers deal with the problem of local ambiguity in part by modifying previously built structure when the chosen structure later proves to be inconsistent. This modification of existing structure distinguishes repair parsing from parallel or multi-path parsing, least-commitment parsing, backtracking, or reparsing strategies. Parsers with a limited capability for repair are psycholinguistically important because they can potentially explain the contrasts between difficult garden path structures (when repair fails) and unproblematic local ambiguities (when repair is successful or easy). Although the idea of repair has been implicit in some psycholinguistic work (and emerged explicitly in the diagnosis model of Fodor & Inoue, 1994, and the NL-Soar model of Lewis, 1993), there has been no clear formulation of the general class of repair parsers. This chapter makes a first step toward such a formulation, show..