tailieunhanh - Báo cáo khoa học: "Learning Predictive Structures for Semantic Role Labeling of NomBank"

This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear multi-task learning algorithm, which extracts the common structures of multiple tasks to improve accuracy, via the use of auxiliary problems. In this paper, we explore a number of different auxiliary problems, and we are able to significantly improve the accuracy of the NomBank SRL task using this approach. To our knowledge, our proposed approach achieves the highest accuracy published to date on the English NomBank SRL task. . | Learning Predictive Structures for Semantic Role Labeling of NomBank Chang Liu and Hwee Tou Ng Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 liuchanl nght @ Abstract This paper presents a novel application of Alternating Structure Optimization ASO to the task of Semantic Role Labeling SRL of noun predicates in NomBank. ASO is a recently proposed linear multi-task learning algorithm which extracts the common structures of multiple tasks to improve accuracy via the use of auxiliary problems. In this paper we explore a number of different auxiliary problems and we are able to significantly improve the accuracy of the Nom-Bank SRL task using this approach. To our knowledge our proposed approach achieves the highest accuracy published to date on the English NomBank SRL task. 1 Introduction The task of Semantic Role Labeling SRL is to identify predicate-argument relationships in natural language texts in a domain-independent fashion. In recent years the availability of large human-labeled corpora such as PropBank Palmer et al. 2005 and FrameNet Baker et al. 1998 has made possible a statistical approach of identifying and classifying the arguments of verbs in natural language texts. A large number of SRL systems have been evaluated and compared on the standard data set in the CoNLL shared tasks Carreras and Marquez 2004 Carreras and Marquez 2005 and many systems have performed reasonably well. Compared to the previous CoNLL shared tasks noun phrase bracketing chunking clause identification and named entity recognition SRL represents a significant step 208 towards processing the semantic content of natural language texts. Although verbs are probably the most obvious predicates in a sentence many nouns are also capable of having complex argument structures often with much more flexibility than its verb counterpart. For example compare affect and effect subj Auto prices arg-ext greatly pred affect obj the .