Computational Linguistics - Summarization
Lexical chains for question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
HLT '93 Proceedings of the workshop on Human Language Technology
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
RACAI: meaning affinity models
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SemEval-2010 task 17: All-words word sense disambiguation on a specific domain
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval-2010 task 17: All-words word sense disambiguation on a specific domain
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
RACAI: Unsupervised WSD experiments @ SemEval-2, task #17
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
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This paper presents an unsupervised word sense disambiguation. (WSD) algorithm that makes use of lexical chains concept [6] to quantify the degree of semantic relatedness between two words. Essentially, the WSD algorithm will try to maximize this semantic measure over a graph of content words in a given sentence in order to perform the disambiguation.