In the field of empirical natural language processing, researchers constantly deal with large amounts of marked-up data; whether the markup is done by the researcher or someone else, human nature dictates that it will have errors in it. This paper will more fully characterise the problem and discuss whether and when (and how) to correct the errors. The discussion is illustrated with specific examples involving function tagging in the Penn treebank.
Best-first chart parsing utilises a figure of merit (FOM) to efficiently guide a parse by first attending to those edges judged better. In the past it has usually been static; this paper will show that with some extra information, a parser can compensate for FOM flaws which otherwise slow it down. Our results are faster than the prior best by a factor of 2.5; and the speedup is won with no significant decrease in parser accuracy.
We demonstrate visually why doubling capacity is the better strategy when resizing arrays. The visual proof makes simple amortised analysis more accessible to a CS2 audience.
Traditional processes for homework assignments are not always a good fit for the sorts of problems often seen in computer science classes. We present our experiences in implementing policies designed to encourage students to involve the instructor and fellow students in their learning process. By shifting to group assignments and permitting students a revision cycle, we improve student satisfaction and maintain or increase student outcomes while decreasing the instructor's grading load.
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