Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label ...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
Accurate application traffic classification and identification are important for network monitoring and analysis. The accuracy of traditional Internet application traffic classific...
Byungchul Park, Young J. Won, Mi-Jung Choi, Myung-...
This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discriminative approach...