Abstract
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 language | English |
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Title of host publication | 34th Australasian Computer Science Conference |
Editors | M Reynolds |
Place of Publication | Sydney |
Publisher | Australian Computer Society |
Pages | 1-8 |
Number of pages | 8 |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 34th Australasian Computer Science Conference (ACSC 2011) - Perth, Perth, Australia Duration: 17 Jan 2011 → 20 Jan 2011 https://50years.acs.org.au/content/dam/acs/50-years/journals/crpit/Vol113.pdf |
Conference
Conference | 34th Australasian Computer Science Conference (ACSC 2011) |
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Abbreviated title | ACSC 2011 |
Country/Territory | Australia |
City | Perth |
Period | 17/01/11 → 20/01/11 |
Internet address |