Automated detection of the first document reporting each new event in temporally-sequenced streams of documents is an open challenge. In this paper we propose a new approach which...
Yiming Yang, Jian Zhang, Jaime G. Carbonell, Chun ...
In this paper, we study the problem of learning block classification models to estimate block functions. We distinguish general models, which are learned across multiple sites, an...
NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combinatio...
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...