Investigation of weakly supervised learning for semantic role labeling

Joo Young Lee, Young In Song, Hae Chang Rim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, we investigate the possibility of the weakly supervised learning for Semantic Role Labeling. First, we attempt to achieve feature splitting which is the base constraint of co-training, and examine if co-training works for the task of Semantic Role Labeling. We also examine the possibility of self-training which uses the identical features with co-training, and compare the performance of cotraining and self-training. From the experiments, we found some interesting points about Semantic Role Labeling task and the weakly supervised learning. As far as we know, this is the first experiment to apply weakly supervised learning to Semantic Role Labeling and the experimental results show that Semantic Role Labeling can be successfully done by weakly supervised learning.

Original languageEnglish
Title of host publicationProceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology
Pages165-170
Number of pages6
DOIs
Publication statusPublished - 2007
Event6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007 - Luoyang, Henan, China
Duration: 2007 Aug 222007 Aug 24

Publication series

NameProceedings - ALPIT 2007 6th International Conference on Advanced Language Processing and Web Information Technology

Other

Other6th International Conference on Advanced Language Processing and Web Information Technology, ALPIT 2007
CountryChina
CityLuoyang, Henan
Period07/8/2207/8/24

ASJC Scopus subject areas

  • Computer Science(all)
  • Information Systems

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