The weighted majority algorithm
Information and Computation
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic lexical acquisition based on statistical distributions
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
HLT '93 Proceedings of the workshop on Human Language Technology
Assigning verbs to semantic classes via WordNet
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Improving WSD with multi-level view of context monitored by similarity measure
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Classifier optimization and combination in the English all words task
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
An API for measuring the relatedness of words in Wikipedia
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Applying alternating structure optimization to word sense disambiguation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Word domain disambiguation via word sense disambiguation
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
PNNL: a supervised maximum entropy approach to word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
On the use of automatically acquired examples for all-nouns word sense disambiguation
Journal of Artificial Intelligence Research
Optimizing classifier performance in word sense disambiguation by redefining word sense classes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploiting internal and external semantics for the clustering of short texts using world knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
The noisy channel model for unsupervised word sense disambiguation
Computational Linguistics
GPLSI-IXA: Using semantic classes to acquire monosemous training examples from domain texts
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
Spanish all-words semantic class disambiguation using Cast3LB corpus
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
An experimental study on unsupervised graph-based word sense disambiguation
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
An open-source toolkit for mining Wikipedia
Artificial Intelligence
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Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense tagged corpus, in order to alleviate the knowledge acquisition bottleneck. We show that these general concepts can be transformed to fine grained word senses using simple heuristics, and applying the technique for recent SENSEVAL data sets shows that our approach can yield state of the art performance.