We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic mo...
The availability of summary data for XML documents has many applications, from providing users with quick feedback about their queries, to cost-based storage design and query opti...
Juliana Freire, Jayant R. Haritsa, Maya Ramanath, ...
This paper studies how to incorporate side information (such as users’ feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studie...