Discrete Mathematics - Topics on domination
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
Personalizing PageRank for word sense disambiguation
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
An Algorithm to Discover the k-Clique Cover in Networks
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial 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
UMCC-DLSI: Integrative resource for disambiguation task
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
An algorithm for finding a maximum clique in a graph
Operations Research Letters
A graph-based approach to WSD using relevant semantic trees and n-cliques model
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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This paper presents a new approach to solve semantic ambiguity using an adaptation of the Cliques Partitioning Technique to N distance. This new approach is able to identify sets of strongly related senses using a multidimensional graph based on different resources: WordNet Domains, SUMO and WordNet Affects. As a result, each Clique will contain relevant information used to extract the correct sense of each word. In order to evaluate our approach there have been conducted several experiments using the data set of the "English All Words" task of Senseval-2 obtaining promising results.