| Kevin Duh|
(Alternative names for this author)
|Co-authors||Ching-Man Au Yeung, Iwata T., Masaaki Nagata, Neubig G., Yeung C.-M.A.|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Kevin Duh is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|How much is said in a tweet? A multilingual, information-theoretic perspective||AAAI Spring Symposium - Technical Report||English||2013||This paper describes a multilingual study on how much information is contained in a single post of microblog text from Twitter in 26 different languages. In order to answer this question in a quantitative fashion, we take an information-theoretic approach, using entropy as our criterion for quantifying "how much is said" in a tweet. Our results find that, as expected, languages with larger character sets such as Chinese and Japanese contain more information per character than other languages. However, we also find that, somewhat surprisingly, information per character does not have a strong correlation with information per microblog post, as authors of microblog posts in languages with more information per character do not necessarily use all of the space allotted to them. Finally, we examine the relative importance of a number of factors that contribute to whether a language has more or less information content in each character or post, and also compare the information content of microblog text with more traditional text from Wikipedia. Copyright||0||0|
|Managing information disparity in multilingual document collections||Algorithms
|ACM Transactions on Speech and Language Processing||English||2013||Information disparity is a major challenge with multilingual document collections. When documents are dynamically updated in a distributed fashion, information content among different language editions may gradually diverge. We propose a framework for assisting human editors to manage this information disparity, using tools from machine translation and machine learning. Given source and target documents in two different languages, our system automatically identifies information nuggets that are new with respect to the target and suggests positions to place their translations. We perform both real-world experiments and large-scale simulations on Wikipedia documents and conclude our system is effective in a variety of scenarios.||0||0|
|Providing cross-lingual editing assistance to Wikipedia editors||CICLing||English||2011||0||0|