Multi-Task Learning in Conditional Random Fields for Chunking in Shallow Semantic Parsing
Saike He, Xiaojie Wang, Dong Yuan, Taozheng Zhang, Xue Bai · Institutional Repositories DataBase (IRDB) · 2009
Alternating Structure Optimization (ASO) is a recently proposed linear Multitask Learning algorithm.Although its effective has been verified in both semi-supervised as well as supervised methods, yet they necessitate taking external resource as a prerequisite.Therefore, feasibility of employing ASO to further improve the performance merely rests on the labeled data on hand proves to be a task deserving close scrutiny.Catering to this challenging while untapped problem, this paper presents a novel application of ASO to the subtask of Shallow Semantic Parsing: Chunking.Our experiments on Chinese Treebank 5.0 present promising result in chunk analysis, and the error rate is reduced by 5.72%, proposing a profound way to further improve the performance.