P
Paul Cook
Researcher at University of New Brunswick
Publications - 74
Citations - 3276
Paul Cook is an academic researcher from University of New Brunswick. The author has contributed to research in topics: Word (computer architecture) & SemEval. The author has an hindex of 26, co-authored 74 publications receiving 2938 citations. Previous affiliations of Paul Cook include University of Toronto & University of Melbourne.
Papers
More filters
Proceedings Article
Simpler unsupervised POS tagging with bilingual projections
TL;DR: An unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus is presented, which automatically identifies “good” training sentences from the parallel corpus and applies self-training.
Proceedings ArticleDOI
Android authorship attribution through string analysis
TL;DR: This work proposes to develop a lightweight system that can generate signatures of malware writers by leveraging the string components present in their Android binaries, and can effectively detect a wide range of existing, as well as any new, malware samples generated by particular authors.
Proceedings Article
unimelb: Topic Modelling-based Word Sense Induction for Web Snippet Clustering
TL;DR: This system adopts a preexisting Word Sense Induction (WSI) methodology based on Hierarchical Dirichlet Process (HDP), a non-parametric topic model, and is shown to perform well in the shared task.
Proceedings ArticleDOI
Exploring Methods and Resources for Discriminating Similar Languages
TL;DR: This paper describes the submissions made by team UniMelb-NLP, which took part in both the closed and open categories of the Discriminating between Similar Languages shared task, and presents the text representations and modeling techniques used.
Proceedings Article
unimelb: Topic Modelling-based Word Sense Induction
TL;DR: This paper describes a previously-proposed WSI methodology for the task, which is based on a Hierarchical Dirichlet Process (HDP), a nonparametric topic model, which requires no parameter tuning, uses the English ukWaC as an external resource, and achieves encouraging results over the shared task.