Web page feature selection and classification using neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Which "Apple" are you talking about ?
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Critical analysis of WSD algorithms
Proceedings of the International Conference on Advances in Computing, Communication and Control
Classification of web documents using concept extraction from ontologies
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
WSEAS Transactions on Computers
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Word Sense Disambiguation (WSD) is main task in the area of natural language processing (NLP). Supervised WSD methods are shown to be more effective than other WSD methods with the limitation of the size of manual annotated learning set. On the other hand, Concept graph is a weighted graph with each of its edges representing the relationships between concepts (relevancy of each pair of concepts). In this paper, we propose a method to improve the retrieval and classification performance of documents from different sources by means of concept graph. In our method, some features are initially selected from a training set by applying a well-known feature selection algorithm. Then, by injecting suggested relevant words for each class from the concept graph, a more enriched feature set is produced to apply to the test set. Our experimental results exhibit an improvement of 14.6% and 18.4% (few and more term injection evaluations, respectfully) in classification and also some improvements in retrieval performance.