Ichiro Yamada, Timothy Baldwin^*1, Hideki Sumiyoshi, Masahiro Shibata, Nobuyuki Yagi
*1 University of Melbourne
IEICE Transactions on Information and Systems　
vol.E90-D, no.10, 2007, pp.1534-1541

This paper presents a method to automatically acquire a given noun's telic and agentive roles from corpus data. These relations form part of the qualia structure assumed in the generative lexicon, where the telic role represents a typical purpose of the entity and the agentive role represents the origin of the entity. Our proposed method employs a supervised machine-learning technique which makes use of template-based contextual features derived from token instances of each noun. We also propose a variant of Spearman's rank correlation to evaluate the correlation of two top-N item lists. Using this correlation method, we represent the ability of the proposed method to identify qualia structure relative to a conventional template-based method.
[DOI:10.1093/ietisy/e90-d.10.1534] permission number 08RB0121

Atsushi Matsui, Simon Clippingdale, Takashi Matsumoto^*1
*1 Faculty of Science and Engineering, Waseda University
IEICE Transactions on Information and Systems　vol.E90-D, no.8, 2007, p.1151-1159

This paper proposes probabilistic pruning techniques for a Bayesian video face recognition system. The system selects the most probable face model using model posterior distributions, which can be calculated using a Sequential Monte Carlo (SMC) method. A combination of two new pruning schemes at the resampling stage significantly boosts computational efficiency by comparison with the original online learning algorithm. Experimental results demonstrate that this approach achieves better performance in terms of both processing time and ID error rate than a conventional approach with a time decay scheme.
[DOI:10.1093/ietisy/e90-d.8.1151] permission number 08RB0122