Academic Paper |
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| Title: | Building Instance Knowledge Network for Word Sense Disambiguation |
| Paper URL: | http://www.ict.swin.edu.au/personal/shu/acsc2011.pdf |
| Author: | Shangfeng Hu |
| Email: | click here to access email |
| Author: | Chengfei Liu |
| Email: | click here to access email |
| Institution: | Swinburne University of Technology |
| Author: | Xiaohui Zhao |
| Email: | click here to access email |
| Institution: | Eindhoven University of Technology |
| Author: | Marek Kowalkiewicz |
| Email: | click here to access email |
| Institution: | SAP Research |
| Linguistic Field: | Computational Linguistics; Semantics |
| Subject Language: |
English
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| Abstract: |
In this paper, a new high precision focused 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. |
| Type: | Individual Paper |
| Status: | Completed |
| Venue: | 2011 Australasian Computer Science Conference (ACSC 2011) |
| URL: | http://www.ict.swin.edu.au/personal/shu/acsc2011.pdf |
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