Sean Massung

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This paper describes the incorporation of the IEEE-TCPP Curriculum Initiative into CS 2 at the University of Illinois at Urbana-Champaign. With control over only one course that requires a semi-rigid curriculum, we detail a sequence of three lessons that explore the basics of parallelism in a visual manner. We draw a contrast between standard teaching(More)
We propose and study novel text representation features created from parse tree structures. Unlike the traditional parse tree features which include all the attached syntactic categories to capture linguistic properties of text, the new features are solely or primarily defined based on the tree structure, and thus better reflect the pure structural(More)
In this year’s WMT translation task, Finnish-English was introduced as a language pair of competition for the first time. We present experiments examining several variations on a morphologically-aware statistical phrase-based machine translation system for translating Finnish into English. Our system variations attempt to mitigate the issue of rich(More)
In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with(More)
We describe SyntacticDiff, a novel, general, and efficient edit-based method for transforming sequences of words given a reference text collection. These transformations can be used directly or can be employed as features to represent text data in a wide variety of text mining applications. As case studies, we apply SyntacticDiff to three quite different(More)
We prove that log-linearly interpolated backoff language models can be efficiently and exactly collapsed into a single normalized backoff model, contradicting Hsu (2007). While prior work reported that log-linear interpolation yields lower perplexity than linear interpolation, normalizing at query time was impractical. We normalize the model offline in(More)
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