Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
KnowNet: building a large net of knowledge from the web
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Quality assessment of large scale knowledge resources
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Graph connectivity measures for unsupervised word sense disambiguation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
English tasks: all-words and verb lexical sample
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Incorporating coreference resolution into word sense disambiguation
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
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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.