Incorporating Multi-task Learning in Conditional Random Fields for Chunking in Semantic Role Labeling
This paper presents a novel application of incorporating Alternating Structure Optimization (ASO) to conduct the task of text chunking of Semantic Role Labeling (SRL) in Chinese texts. ASO is a competent linear algorithm based on the theory of Multi-task Learning. In this paper, by constructing several SRL tasks to constitute a multi-task, we are able to encode the inference obtained by ASO algorithm as additional feature to further boost the performance of the target task employing Conditional Random Fields （CRFs）. To our knowledge, our method is the first that incorporates multi-task learning into a statistical model in SRL for Chinese texts. We evaluate our approach on Penn Treebank data sets and obtain encouraging result.
Tracking SRL ASO multi-task learning CRFs
Saike HE Taozheng ZHANG Xue BAI Xiaojie WANG and Yuan DONG
School of Information & Communication Engineering of Beijing University of Posts and Telecommunicati School of Computer Science and Technology of Beijing University of Posts and Telecommunications, 100 School of Information & Communication Engineering of Beijing University of Posts and Telecommunicati
国际会议
大连
英文
1-5
2009-09-24（万方平台首次上网日期，不代表论文的发表时间）