Peter Z. Yeh, Bruce Porter, Ken Barker
In this paper, we present a unified knowledge based approach for sense disambiguation and semantic role labeling. Our approach performs both tasks through a single algorithm that matches candidate semantic interpretations to background knowledge to select the best matching candidate. We evaluate our approach on a corpus of sentences collected from various domains and show how our approach performs well on both sense disambiguation and semantic role labeling.
Subjects: 13. Natural Language Processing; 10. Knowledge Acquisition