Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow ...Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.展开更多
Suppose that f（ z ） is a transcendental entire function and that F（f） contains unbounded Fatou components. In this article, we obtained some links between the lowor bounds of the lower order of f and the angle of ...Suppose that f（ z ） is a transcendental entire function and that F（f） contains unbounded Fatou components. In this article, we obtained some links between the lowor bounds of the lower order of f and the angle of an angular sector which is completely contained in an unbounded Fatou component of F（f）. Then, we investigate the bounded components for the Julia set J（f） of a transcendental entire function f（z ） and obtain a sufficient and necessary condition.展开更多
基金The National Natural Science Foundation of China(No60663004)the PhD Programs Foundation of Ministry of Educa-tion of China (No20050007023)
文摘Due to the fact that semantic role labeling （SRL） is very necessary for deep natural language processing, a method based on conditional random fields （CRFs） is proposed for the SRL task. This method takes shallow syntactic parsing as the foundation, phrases or named entities as the labeled units, and the CRFs model is trained to label the predicates＇ semantic roles in a sentence. The key of the method is parameter estimation and feature selection for the CRFs model. The L-BFGS algorithm was employed for parameter estimation, and three category features： features based on sentence constituents, features based on predicate, and predicate-constituent features as a set of features for the model were selected. Evaluation on the datasets of CoNLL-2005 SRL shared task shows that the method can obtain better performance than the maximum entropy model, and can achieve 80. 43 % precision and 63. 55 % recall for semantic role labeling.
文摘Suppose that f（ z ） is a transcendental entire function and that F（f） contains unbounded Fatou components. In this article, we obtained some links between the lowor bounds of the lower order of f and the angle of an angular sector which is completely contained in an unbounded Fatou component of F（f）. Then, we investigate the bounded components for the Julia set J（f） of a transcendental entire function f（z ） and obtain a sufficient and necessary condition.
