Building Instance Knowledge Network for Word Sense Disambiguation

Shangfeng Hu, Chengfei Liu, Xiaohui ZHAO, Marek Kowalkiewicz

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

1 Citation (Scopus)


In this paper, a new high precision focused word sense disambiguation (WSD) approach is proposed, which not only attempts to identify the proper sense for a word but also provides the probabilistic evaluation for the identification confidence at the same time. A novel Instance Knowledge Network (IKN) is built to generate and maintain semantic knowledge at the word, type synonym set and instance levels. Related algorithms based on graph matching are developed to train IKN with probabilistic knowledge and to use IKN for probabilistic word sense disambiguation. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements in different precision ranges and the rationality of probabilistic based automatic confidence evaluation of disambiguation. We combine our WSD algorithm with five best WSD algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms
Original languageEnglish
Title of host publication34th Australasian Computer Science Conference
EditorsM Reynolds
Place of PublicationSydney
PublisherAustralian Computer Society
Number of pages8
Publication statusPublished - 2011
Externally publishedYes
Event34th Australasian Computer Science Conference (ACSC 2011) - Perth, Perth, Australia
Duration: 17 Jan 201120 Jan 2011


Conference34th Australasian Computer Science Conference (ACSC 2011)
Abbreviated titleACSC 2011
Internet address


Dive into the research topics of 'Building Instance Knowledge Network for Word Sense Disambiguation'. Together they form a unique fingerprint.

Cite this