Incorporating frame information to semantic role labeling

Joo Young Lee, Young In Song, Hae Chang Rim, Kyoung Soo Han

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we suggest a new probabilistic model of semantic role labeling, which uses the frameset of the predicate as explicit linguistic knowledge for providing global information on the predicateargument structure that local classifier is unable to catch. The proposed model consists of three sub-models: role sequence generation model, frameset generation model, and matching model. The role sequence generation model generates the semantic role sequence candidates of a given predicate by using the local classification approach, which is a widely used approach in previous research. The frameset generation model estimates the probability of each frameset that the predicate can take. The matching model is designed to measure the degree of the matching between the generated role sequence and the frameset by using several features. These features are developed to represent the predicate-argument structure information described in the frameset. In the experiments our model shows that the use of knowledge about the predicate-argument structure is effective for selecting a more appropriate semantic role sequence.

Original languageEnglish
Pages (from-to)201-204
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number1
Publication statusPublished - 2010


  • Frame information
  • Frameset
  • Predicate-argument structure
  • Propbank
  • Semantic role labeling

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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