This paper presents an unsupervised learning approach to disambiguate various relations between name entities by use of various lexical and syntactic features from the contexts. I...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...
Adaptor grammars (Johnson et al., 2007b) are a non-parametric Bayesian extension of Probabilistic Context-Free Grammars (PCFGs) which in effect learn the probabilities of entire s...
Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best des...
Many biological propositions can be supported by a variety of different types of evidence. It is often useful to collect together large numbers of such propositions, together with...
Philip M. Long, Vinay Varadan, Sarah Gilman, Mark ...
Abstract. The manual acquisition and modeling of tourist information as e.g. addresses of points of interest is time and, therefore, cost intensive. Furthermore, the encoded inform...