We propose a method to adapt an existing relation extraction system to extract new relation types with minimum supervision. Our proposed method comprises two stages: learning a lo...
At Google, experimentation is practically a mantra; we evaluate almost every change that potentially affects what our users experience. Such changes include not only obvious user-...
Diane Tang, Ashish Agarwal, Deirdre O'Brien, Mike ...
Abstract. This work proposes a family of language-independent semantic kernel functions defined for individuals in an ontology. This allows exploiting wellfounded kernel methods fo...
In this paper, we propose a semi-supervised learning approach for classifying program (bot) generated web search traffic from that of genuine human users. The work is motivated by...
Hongwen Kang, Kuansan Wang, David Soukal, Fritz Be...
Intelligent desktop assistants could provide more help for users if they could learn models of the users’ workflows. However, discovering desktop workflows is difficult becau...
Jianqiang Shen, Erin Fitzhenry, Thomas G. Dietteri...