Most existing automatic taxonomy induction systems exploit one or more features to induce a taxonomy; nevertheless there is no systematic study examining which are the best featur...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Attacks against privileged applications can be detected by analyzing the stream of system calls issued during process execution. In the last few years, several approaches have been...
Darren Mutz, William K. Robertson, Giovanni Vigna,...
We describe the first tractable Gibbs sampling procedure for estimating phrase pair frequencies under a probabilistic model of phrase alignment. We propose and evaluate two nonpar...
We propose a high-performance cascaded hybrid model for Chinese NER. Firstly, we use Boosting, a standard and theoretically wellfounded machine learning method to combine a set of...